Date: (Tue) Nov 10, 2015

Introduction:

Data: Source: Training: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv
New: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv
Time period:

Synopsis:

Based on analysis utilizing <> techniques, :

Regression results: First run:

Classification results: template: prdline.my == “Unknown” -> 296 Low.cor.X.glm: Leaderboard: 0.83458 -> Rank 288 / 1884 0.85514 newobs_tbl=[N=471, Y=327]; submit_filename=template_Final_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=76]=201; max.Accuracy.OOB=0.7710; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=95.42; productline=49.22; D.T.like=29.75; D.T.use=26.32; D.T.box=21.53;

prdline: -> Worse than template prdline.my == “Unknown” -> 285 All.X.no.rnorm.rf: Leaderboard: 0.82649 newobs_tbl=[N=485, Y=313]; submit_filename=prdline_Final_rf_submit.csv OOB_conf_mtrx=[YN=119, NY=80]=199; max.Accuracy.OOB=0.8339; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=84.25; D.sum.TfIdf=7.28; D.T.use=4.26; D.T.veri=2.78; D.T.scratch=1.99; D.T.box=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.81234 newobs_tbl=[N=471, Y=327]; submit_filename=prdline_Low_cor_X_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=74]=199; max.Accuracy.OOB=0.8205; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=96.07; prdline.my=51.37; D.T.like=29.39; D.T.use=25.43; D.T.box=22.27; D.T.veri=; D.T.scratch=;

oobssmpl: -> Low.cor.X.glm: Leaderboard: 0.83402 newobs_tbl=[N=440, Y=358]; submit_filename=oobsmpl_Final_glm_submit OOB_conf_mtrx=[YN=114, NY=84]=198; max.Accuracy.OOB=0.7780; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=93.87; prdline.my=60.48; D.sum.TfIdf=; D.T.condition=8.69; D.T.screen=7.96; D.T.use=7.50; D.T.veri=; D.T.scratch=;

category: -> Low.cor.X.glm: Leaderboard: 0.82381 newobs_tbl=[N=470, Y=328]; submit_filename=category_Final_glm_submit OOB_conf_mtrx=[YN=119, NY=57]=176; max.Accuracy.OOB=0.8011; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=79.19; prdline.my=55.22; D.sum.TfIdf=; D.T.ipad=27.05; D.T.like=21.44; D.T.box=20.67; D.T.condition=; D.T.screen=;

dataclns: -> All.X.no.rnorm.rf: Leaderboard: 0.82211 newobs_tbl=[N=485, Y=313]; submit_filename=dataclns_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=75]=179; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=65.85; prdline.my=7.74; D.sum.TfIdf=; D.T.use=2.01; D.T.condition=1.87; D.T.veri=1.62; D.T.ipad=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.79264 newobs_tbl=[N=460, Y=338]; submit_filename=dataclns_Low_cor_X_glm_submit OOB_conf_mtrx=[YN=113, NY=74]=187; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 -> different from prev run of 0.6 biddable=100.00; startprice.log=91.85; prdline.my=38.34; D.sum.TfIdf=; D.T.ipad=29.92; D.T.box=27.76; D.T.work=25.79; D.T.use=; D.T.condition=;

txtterms: -> top_n = c(10) Low.cor.X.glm: Leaderboard: 0.81448 newobs_tbl=[N=442, Y=356]; submit_filename=txtterms_Final_glm_submit OOB_conf_mtrx=[YN=113, NY=69]=182; max.Accuracy.OOB=0.7943; opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=90.11; prdline.my=37.65; D.sum.TfIdf=; D.T.ipad=28.67; D.T.work=24.90; D.T.great=21.44; # [1] “D.T.condit” “D.T.condition” “D.T.good” “D.T.ipad” “D.T.new”
# [6] “D.T.scratch” “D.T.screen” “D.T.this” “D.T.use” “D.T.work”

All.X.glm: Leaderboard: 0.81016
    newobs_tbl=[N=445, Y=353]; submit_filename=txtterms_Final_glm_submit
    OOB_conf_mtrx=[YN=108, NY=72]=180; max.Accuracy.OOB=0.7966;
        opt.prob.threshold.OOB=0.5
        biddable=100.00; startprice.log=88.24; prdline.my=33.81; D.sum.TfIdf=; 
        D.T.scratch=25.51; D.T.use=18.97; D.T.good=16.37; 

[1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.great” “D.T.excel” “D.T.work” “D.T.ipad”

Max.cor.Y.rpart: Leaderboard: 0.79258
    newobs_tbl=[N=439, Y=359]; submit_filename=txtterms_Final_rpart_submit
    OOB_conf_mtrx=[YN=105, NY=76]=181; max.Accuracy.OOB=0.7954802;
        opt.prob.threshold.OOB=0.5
        startprice.log=100; biddable=; prdline.my=; D.sum.TfIdf=; 
        D.T.scratch=; D.T.use=; D.T.good=; 

[1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”

All.X.no.rnorm.rf: Leaderboard: 0.80929
    newobs_tbl=[N=545, Y=253]; submit_filename=txtterms_Final_rf_submit
    OOB_conf_mtrx=[YN=108, NY=61]=169; max.Accuracy.OOB=0.8090395
        opt.prob.threshold.OOB=0.5
        startprice.log=100.00; biddable=78.82; idseq.my=63.43; prdline.my=45.57;
        D.T.use=2.76; D.T.condit=2.35; D.T.scratch=2.00; D.T.good=; 

[1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”

txtclstr: All.X.no.rnorm.rf: Leaderboard: 0.79363 -> 0.79573 newobs_tbl=[N=537, Y=261]; submit_filename=txtclstr_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=61]=165; max.Accuracy.OOB=0.8135593 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=79.99; idseq.my=64.94; prdline.my=4.14; prdline.my.clusterid=1.15; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”

dupobs: All.X.no.rnorm.rf: Leaderboard: 0.79295 newobs_tbl=[N=541, Y=257]; submit_filename=dupobs_Final_rf_submit OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=94.49; idseq.my=67.40; prdline.my=4.48; prdline.my.clusterid=1.99; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”

All.X.no.rnorm.rf: Leaderboard: 0.79652
    newobs_tbl=[N=523, Y=275]; submit_filename=dupobs_Final_rf_submit
    OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401
        opt.prob.threshold.OOB=0.5
        startprice.log=100.00; biddable=94.24; idseq.my=67.92; 
            prdline.my=4.33; prdline.my.clusterid=2.17; 

[1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”

csmmdl: All.X.no.rnorm.rf: Leaderboard: 0.79396 newobs_tbl=[N=525, Y=273]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=111, NY=66]=177; max.Accuracy.OOB=0.8000000 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=90.30; idseq.my=67.06; prdline.my=4.40; cellular.fctr=3.57; prdline.my.clusterid=2.08;

All.Interact.X.no.rnorm.rf: Leaderboard: 0.77867 newobs_tbl=[N=564, Y=234]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=120, NY=53]=173; max.Accuracy.OOB=0.8045198 opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=93.99; idseq.my=57.30; prdline.my=9.09; cellular.fctr=3.30; prdline.my.clusterid=2.35;

All.Interact.X.no.rnorm.rf: Leaderboard: 0.77152 newobs_tbl=[N=539, Y=259]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=, NY=]=; max.Accuracy.OOB=0.8011299 opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=94.93; idseq.my=57.12; prdline.my=9.29; cellular.fctr=3.20; prdline.my.clusterid=2.50; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”

    All.X.glmnet: 
        fit_RMSE=???; OOB_RMSE=115.1247; new_RMSE=115.1247; 
        prdline.my.fctr=100.00; condition.fctrNew=88.53; D.npnct09.log=84.34
            biddable=16.48; idseq.my=57.27;

spdiff:
All.Interact.X.no.rnorm.rf: Leaderboard: 0.78218 newobs_tbl=[N=517, Y=281]; submit_filename=spdiff_Final_rf_submit OOB_conf_mtrx=[YN=121, NY=38]=159; max.Accuracy.OOB=0.8203390 opt.prob.threshold.OOB=0.6 biddable=100.00; startprice.diff=57.53; idseq.my=41.31; prdline.my=11.43; cellular.fctr=2.36; prdline.my.clusterid=1.82;

    All.X.no.rnorm.rf: 
        fit_RMSE=92.19; OOB_RMSE=130.86; new_RMSE=130.86; 
        biddable=100.00; prdline.my.fctr=61.92; idseq.my=57.77;
            condition.fctr=29.53; storage.fctr=11.22; color.fctr=6.69;
            cellular.fctr=6.11
            
All.X.no.rnorm.rf: Leaderboard: 0.77443
    newobs_tbl=[N=606, Y=192]; submit_filename=spdiff_Final_rf_submit
    OOB_conf_mtrx=[YN=112, NY=28]=140; max.Accuracy.OOB=0.8418079
        opt.prob.threshold.OOB=0.6
        startprice.diff=100.00; biddable=96.53; idseq.my=38.10; 
            prdline.my=3.65; cellular.fctr=2.21; prdline.my.clusterid=0.91; 

[1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”

color: All.Interact.X.glmnet: fit_RMSE=88.64520; prdline.my.fctr:D.TfIdf.sum.stem.stop.Ratio=100.00; prdline.my.fctr:condition.fctr=77.35 D.TfIdf.sum.stem.stop.Ratio=68.18 prdline.my.fctr:color.fctr=68.12 prdline.my.fctr:storage.fctr=63.32

All.X.no.rnorm.rf: Leaderboard: 0.80638
    newobs_tbl=[N=550, Y=248]; submit_filename=color_Final_rf_submit
    OOB_conf_mtrx=[YN=108, NY=54]=162; max.Accuracy.OOB=0.8169492
        opt.prob.threshold.OOB=0.5
        biddable=100.00; startprice.diff=77.90; idseq.my=48.49; 
            D.ratio.sum.TfIdf.nwrds=6.48; storage.fctr=4.74;
                D.TfIdf.sum.stem.stop.Ratio=4.57; prdline.my=4.32;

[1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”

All.Interact.X.no.rnorm.rf: Leaderboard: 0.72974
    newobs_tbl=[N=682, Y=116]; submit_filename=assctxt_Final_rf_submit
    OOB_conf_mtrx=[YN=125, NY=43]=168; max.Accuracy.OOB=0.8101695; max.auc.OOB=???;
        opt.prob.threshold.OOB=0.6
        biddable=100.00; startprice.diff=51.04; idseq.my=29.51; 
            startprice.diff:biddable=28.70
            prdline.my.fctriPadmini:idseq.my=6.89
    Highest max.auc.OOB=???; for model:        

    All.X.no.rnorm.rf: min.RMSE.fit=1.4967021

biddable 100.00000000 idseq.my 98.00292371 startprice.unit9 34.31130220 prdl.my.descr.fctr 18.10984741 D.ratio.sum.TfIdf.nwrds 15.23549621 color.fctrUnknown 14.05520993 D.TfIdf.sum.stem.stop.Ratio 13.00884673 D.ratio.nstopwrds.nwrds 10.51165302

All.X.gbm: Leaderboard: 0.75430
    newobs_tbl=[N=582, Y=216]; submit_filename=mdlsel_Final_gbm_submit
    OOB_conf_mtrx=[YN=58, NY=65]=123; 
        max.Accuracy.OOB=0.8617978; max.auc.OOB=0.9367161;
        opt.prob.threshold.OOB=0.5

startprice.diff 100.0000000 100.00000000 biddable 66.6475055 65.40764971 idseq.my 1.8632456 4.55963698

splogdiff: All.X.gbm: Leaderboard: 0.70111 newobs_tbl=[N=553, Y=245]; submit_filename=splogdiff_Final_gbm_submit OOB_conf_mtrx=[YN=35, NY=101]=136; max.Accuracy.OOB=0.8471910; max.auc.OOB=0.9388912; opt.prob.threshold.OOB=0.3 startprice.log.diff 100.0000000 100.0000000 biddable 86.8563123 88.0261866 idseq.my 8.3580281 2.9054298
Forum Ideas: I then focused on feature engineering, each new variable brought its own little improvement so in the end i just kept adding new ones and let the models do their thing. Here are some i used: model (productline:storage:condition), isNew, model2 (product:isNew), 50 common words from descr, descrLength, capsFactor (% of caps in description), number of cheaper items of same model2, number of dearer items of same model2, priceFactor (vs. mean of price for model), priceFactor2 (vs. mean of price for model2), bigID (if ID> 11000 because there seems to be a huge drop in sales after some time), timeline (year of product launch, reasoning is you want to spend less money on older products).

Get the median startprice for each level of productline and condition. Take the difference from startprice as a new variable. I find median works much better than the mean since startprice is not normally distributed. I also created another binary variable on whether this difference is positive or negative.

Square root startprice

scale and center all the variables except sold, including the dummies.

Prediction Accuracy Enhancement Options:

  • import.data chunk:
    • which obs should be in fit vs. OOB (currently dirty.0 vs .1 is split 50%)
  • inspect.data chunk:
    • For date variables
      • Appropriate factors ?
      • Different / More last* features ?
  • scrub.data chunk:
  • transform.data chunk:
    • derive features from multiple features
  • manage.missing.data chunk:
    • Not fill missing vars
    • Fill missing numerics with a different algorithm
    • Fill missing chars with data based on clusters
  • extract.features chunk:
    • Text variables: move to date extraction chunk ???
      • Mine acronyms
      • Mine places
  • Review set_global_options chunk after features are finalized

[](.png)

Potential next steps include:

  • Organization:
    • Categorize by chunk
    • Priority criteria:
      1. Ease of change
      2. Impacts report
      3. Cleans innards
      4. Bug report
  • all chunks:
    • at chunk-end rm(!glb_)
  • manage.missing.data chunk:
    • cleaner way to manage re-splitting of training vs. new entity
  • extract.features chunk:
    • Add n-grams for glbFeatsText
      • “RTextTools”, “tau”, “RWeka”, and “textcat” packages
    • Convert user-specified mutate code to config specs
  • fit.models chunk:
    • Prediction accuracy scatter graph:
    • Add tiles (raw vs. PCA)
    • Use shiny for drop-down of “important” features
    • Use plot.ly for interactive plots ?

    • Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
    • move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
    • create a custom model for rpart that has minbucket as a tuning parameter
    • varImp for randomForest crashes in caret version:6.0.41 -> submit bug report

  • Probability handling for multinomials vs. desired binomial outcome
  • ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
  • extensions toward multiclass classification are scheduled for the next release

  • Skip trControl.method=“cv” for dummy classifier ?
  • Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
  • glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented

  • fit.all.training chunk:
    • myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
  • Compare glb_sel_mdl vs. glb_fin_mdl:
    • varImp
    • Prediction differences (shd be minimal ?)
  • Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
  • Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
  • Parameterize glb_analytics_pn
  • Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
  • Replicate myfit_mdl_classification features in myfit_mdl_regression
  • Do non-glm methods handle interaction terms ?
  • f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
  • Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
  • Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
  • Incorporate code chunks in print_sessionInfo
  • Test against
    • projects in github.com/bdanalytics
    • lectures in jhu-datascience track

Analysis:

rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")
## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(6) # # of cores on machine - 2
suppressPackageStartupMessages(require(caret))
source("~/Documents/Work/PullRequests/caret/pkg/caret/R/confusionMatrix.R")
source("~/Documents/Work/PullRequests/caret/pkg/caret/R/ggplot.R")
#packageVersion("tm")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")

# Analysis control global variables
# Inputs
glb_trnng_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv"
glb_newdt_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv"
glbInpMerge <- # NULL #: default
    list(fnames = c("ebayipads_finmdl_bid0_sp_out.csv", "ebayipads_mdlens_bid1_sp_out.csv"))

glb_is_separate_newobs_dataset <- TRUE    # or TRUE
    glb_split_entity_newobs_datasets <- FALSE   # select from c(FALSE, TRUE)
    glb_split_newdata_method <- NULL # select from c(NULL, "condition", "sample", "copy")
    glb_split_newdata_condition <- NULL # or "is.na(<var>)"; "<var> <condition_operator> <value>"
    glb_split_newdata_size_ratio <- 0.3               # > 0 & < 1
    glb_split_sample.seed <- 123               # or any integer

glbObsDropCondition <- # default : NULL 
            "(UniqueID %in% c(NULL
                , 11234 #sold=0; 2 other dups(10306, 11503) are sold=1
                , 11844 #sold=0; 3 other dups(11721, 11738, 11812) are sold=1
                )) | 
            (productline %in% c('iPad 5', 'iPad mini Retina'))
                    # | (biddable != 0) # bid0_sp
                    # | (biddable == 0) # bid1_sp
            "
#parse(text=glbObsDropCondition)
#subset(glb_allobs_df, .grpid %in% c(31))
    
glb_obs_repartition_train_condition <- NULL 
#    "!is.na(sold) & (sold == 1)" # : bid._sp

glb_max_fitobs <- NULL # or any integer                         

glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression; 
    glb_is_binomial <- TRUE #or FALSE

glb_rsp_var_raw <- "sold" #: !_sp # "startprice" # : bid._sp # 

# for classification, the response variable has to be a factor
glb_rsp_var <- "sold.fctr" #:!_sp # "startprice.log10" :bid._sp # glb_rsp_var_raw :default

# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"), 
#   or contains spaces (e.g. "Not in Labor Force")
#   caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- function(raw) { # NULL
#     return(raw ^ 0.5)
#     return(log(1 + raw))
#     return(log10(raw)) # bid._sp
#     return(exp(-raw / 2))
    ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
#     #as.factor(paste0("B", raw))
#     #as.factor(gsub(" ", "\\.", raw))    
}
# glb_map_rsp_raw_to_var(tst <- c(NA, 0, 1)) # !_sp
# glb_map_rsp_raw_to_var(tst <- c(NA, 0, 2.99, 280.50, 1000.00)) # bid._sp

glb_map_rsp_var_to_raw <- function(var) { # NULL #
#     return(var ^ 2.0)
#     return(exp(var) - 1)
#     return(10 ^ var) # bid._sp
#     return(-log(var) * 2)
    as.numeric(var) - 1
#     #as.numeric(var)
#     #gsub("\\.", " ", levels(var)[as.numeric(var)])
#     c("<=50K", " >50K")[as.numeric(var)]
#     #c(FALSE, TRUE)[as.numeric(var)]
}
# glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst))

if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
    stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # mdl_id is appended later

# List info gathered for various columns
# <col_name>:   <description>; <notes>
# description = The text description of the product provided by the seller.
# biddable = Whether this is an auction (biddable=1) or a sale with a fixed price (biddable=0).
# startprice = The start price (in US Dollars) for the auction (if biddable=1) or the sale price (if biddable=0).
# condition = The condition of the product (new, used, etc.)
# cellular = Whether the iPad has cellular connectivity (cellular=1) or not (cellular=0).
# carrier = The cellular carrier for which the iPad is equipped (if cellular=1); listed as "None" if cellular=0.
# color = The color of the iPad.
# storage = The iPad's storage capacity (in gigabytes).
# productline = The name of the product being sold.

# If multiple vars are parts of id, consider concatenating them to create one id var
# If glb_id_var == NULL, ".rownames <- row.names()" is the default
# Derive a numeric feature from id var

# User-specified exclusions
# List feats that shd be excluded due to known causation by prediction variable
glbFeatsExclude <- c(NULL
### !_sp
    , "description", "descr.my", "productline"
    , "startprice", "startprice.log10.predict", "sprice.predict.diff"
### bid0_sp                                  
#                                   , "description", "productline"
#                                   , "sold", "startprice.log10.cut.fctr"
#     # List feats that are linear combinations (alias in glm)
#                                 , "D.terms.post.stem.n.log", "D.weight.sum"
#                                 #, "prdl.descr.my.fctriPad4#1:.clusterid.fctr3" This does not work
#     # if RFE is rated lower than Low.cor, list feats that are in RFE & not in Low.cor
#         # min.RMSE.fit(RFE.X.glmnet)=0.1138888
# #             D.chrs.n.log                 61.12483
# #             D.chrs.uppr.n.log            61.12483
# #             D.ratio.wrds.stop.n.wrds.n   61.12483
# #             D.terms.post.stop.n.log      61.12483
# #             D.weight.post.stem.sum       61.12483
# #             D.wrds.n.log                 61.12483
# #             D.wrds.stop.n.log            61.12483
# #             D.wrds.unq.n.log             61.12483
#                             #, "startprice.dcm2.is9" # min.RMSE.fit(RFE.X.glmnet)=0.1141991 (up)
#                             , "D.wrds.stop.n.log"    # min.RMSE.fit(RFE.X.glmnet)=0.1131232
### bid0_sp                            
### bid1_sp                                  
#                                   , "description", "productline"
#                                   , "sold", "startprice.log10.cut.fctr"
### bid1_sp                            
                                  ) 

glb_id_var <- c("UniqueID")
glb_category_var <- "prdl.my.fctr" # "prdl.descr.my.fctr" # "productline" # NULL 
glb_drop_vars <- c(NULL) # or c("<col_name>")

glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"

glb_assign_pairs_lst <- NULL; 
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
#                                            to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)

# Derived features
glbFeatsDerive <- NULL;

# Add logs of numerics that are not distributed normally ->  do automatically ???
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)

# glbFeatsDerive[["prdline.my"]] <- list(
#     mapfn=function(productline) { return(productline) }    
#     , args=c("productline"))

### bid._sp
# glbFeatsDerive[["startprice.log10.cut.fctr"]] <- list(
#     mapfn=function(startprice.log10) { return(cut(startprice.log10, 3)) }    
#     , args=c("startprice.log10"))
### bid._sp
glbFeatsDerive[["sprice.root2"]] <- list(
    mapfn = function(startprice) { return(startprice ^ (1/2)) }    
    , args = c("startprice"))
glbFeatsDerive[["sprice.log10"]] <- list(
    mapfn = function(startprice) { return(log(startprice)) }    
    , args = c("startprice"))
glbFeatsDerive[["sprice.d20nexp"]] <- list(
    mapfn = function(startprice) { return(exp(-startprice / 20)) }    
    , args = c("startprice"))

glbFeatsDerive[["sprice.predict.diff"]] <- list(
    mapfn = function(startprice.log10.predict, startprice) { 
        spdiff <- (10 ^ startprice.log10.predict) - startprice; 
        return(spdiff) }    
    , args = c("startprice.log10.predict", "startprice"))
# glbFeatsDerive[["spdiff.root10"]] <- list(
#     mapfn = function(sprice.predict.diff) { 
#         return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }    
#     , args = c("sprice.predict.diff"))
glbFeatsDerive[["spdiff.cut.fctr"]] <- list(
    mapfn = function(sprice.predict.diff) { 
        return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }    
    , args = c("sprice.predict.diff"))
  
#glb_allobs_df[which(glb_post_stop_words_terms_mtrx_lst[[txt_var]][, subset(glb_post_stop_words_terms_df_lst[[txt_var]], term %in% c("conditionminimal"))$pos] > 0), "description"]
glbFeatsDerive[["descr.my"]] <- list(
    mapfn = function(description) { mod_raw <- description;
### bid._sp
#         # This is here because it does not work with txt_map_filename
        mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ",
                        mod_raw)
#         # This should go into txt_map_filename    
#         mod_raw <- gsub("\\.\\.", "\\. ", mod_raw);    
#         # Don't parse for "." because of ".com"; use customized gsub for that text
#         mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
#mod_raw <- grep("&#034;", glb_allobs_df$descr.my, value = TRUE)        
        mod_raw <- gsub("&amp;", "&", mod_raw);
        mod_raw <- gsub("&lt;", "<", mod_raw);
        mod_raw <- gsub("&gt;", ">", mod_raw);
        mod_raw <- gsub("<br>", " ", mod_raw); # line break - add a count for it ???     
        mod_raw <- gsub("&#034;", " ", mod_raw); # justification meta-character        
        mod_raw <- gsub("&#(0*)37;", "%", mod_raw);        
        mod_raw <- gsub("&#039;", "'", mod_raw);
        mod_raw <- gsub("([[:digit:]])\\.([[:digit:]])\\.([[:digit:]])",
                        "\\1point\\2\\point\\3", mod_raw);        
        mod_raw <- gsub("([[:digit:]])\\.([[:digit:]])", "\\1point\\2", mod_raw);
        mod_raw <- gsub("([[:digit:]]),([[:digit:]])", "\\1\\2", mod_raw);        
        mod_raw <- gsub("\\b1st\\b", "first", mod_raw);        
        mod_raw <- gsub("\\b2nd\\b", "second", mod_raw);
        mod_raw <- gsub("\\b3rd\\b", "third", mod_raw);
        mod_raw <- gsub("\\b4th\\b", "fourth", mod_raw);        
        mod_raw <- gsub("\\.(com|COM)\\b", "dot\\1", mod_raw);        
#         
#         # Modifications for this exercise only
#         # Add dictionary to stemDocument e.g. stickers stemmed to sticker ???
#         mod_raw <- gsub("8\\.25", "825", mod_raw, ignore.case=TRUE);  
        mod_raw <- gsub("\\b10\\.SCREEN\\b", "10\\. SCREEN", mod_raw); 
        mod_raw <- gsub("\\b128 gb\\b", "128gb", mod_raw);  
        mod_raw <- gsub("\\b16G\\b", "16GB", mod_raw);          
#         mod_raw <- gsub(" 16 gig ", " 16gb ", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub(" 16 gb ", " 16gb ", mod_raw, ignore.case=TRUE);        
#         
#         mod_raw <- gsub("\\bAccounts\\b", "Account", mod_raw, ignore.case=FALSE);
#         mod_raw <- gsub("\\bactivated\\b", "activate", mod_raw, ignore.case=FALSE);        
#         mod_raw <- gsub(" actuuly ", " actual ", mod_raw, ignore.case=TRUE);
        mod_raw <- gsub("\\badaptor\\b", "adapter", mod_raw);
#         mod_raw <- gsub("\\baffects\\b", "affect", mod_raw, ignore.case=FALSE);           
        mod_raw <- gsub("\\bair-like\\b", "air -like", mod_raw);
        mod_raw <- gsub("\\bALL-JUST\\b", "ALL -JUST", mod_raw);        
        mod_raw <- gsub("\\bApple's\\b", "Apple'", mod_raw);        
# #mod_raw <- glb_allobs_df[c(1322), txt_var]; mod_raw        
        mod_raw <- gsub("\\bApple care\\b", "Applecare", mod_raw);
        mod_raw <- gsub("\\bAT&T\\b", "ATT", mod_raw);        
        
#         mod_raw <- gsub(" bacK!wiped ", " bacK ! wiped ", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub(" backplate", " back plate", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub("\\bbarley", "barely", mod_raw, ignore.case=TRUE);        
#         mod_raw <- gsub(" bend ", " bent ", mod_raw, ignore.case=TRUE);         
        mod_raw <- gsub("\\b(B|b)(EST|est) (B|b)(UY|uy)\\b", "\\1\\2\\3\\4", mod_raw);
#         mod_raw <- gsub(" black\\.Device ", " black \\. Device ", mod_raw,
#                         ignore.case=TRUE);        
#         mod_raw <- gsub("black\\),charger ", "black\\), charger ", mod_raw,
#                         ignore.case=TRUE);        
#         mod_raw <- gsub("\\bblacked\\b", "black", mod_raw, ignore.case=FALSE);
#         mod_raw <- gsub("\\bblemish\\b", "blemishes", mod_raw, ignore.case=FALSE);        
#         mod_raw <- gsub(" blocks", " blocked", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub(" book ", " manual ", mod_raw, ignore.case=TRUE);            
        mod_raw <- gsub("\\b(B|b)(RAND|rand)( |-)(N|n)(EW|ew)\\b", "\\1\\2\\4\\5", mod_raw)
            #mod_raw <- c("brand new", "BRAND new", "brand NEW", "BRAND NEW", "bbrand new", "brand-new", "brand newb")
        mod_raw <- gsub("\\bbrokenCharger\\b", "broken Charger", mod_raw);
#         
        mod_raw <- gsub("\\bC-Major\\b", "C -Major", mod_raw)    
#         mod_raw <- gsub(" perfectlycord ", " perfectly cord ", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub("\\bcord", "cable", mod_raw, ignore.case=TRUE);     
        mod_raw <- gsub("\\bcables\\.No\\b", "cables. No", mod_raw);        
#         mod_raw <- gsub("\\bcables\\b", "cable", mod_raw, ignore.case=TRUE);        
#         
        mod_raw <- gsub("\\bcare\\.The\\b", "care\\. The", mod_raw);
#         mod_raw <- gsub("\\b(cared|careful|CAREFUL)\\b", "care", mod_raw, ignore.case=FALSE);
#         mod_raw <- gsub("\\b(cases|casing)\\b", "case", mod_raw, ignore.case=TRUE);        
# #mod_raw <- glb_allobs_df[c(88,187,280,1040,1098), txt_var]; mod_raw        
        mod_raw <- gsub("\\bCase/Cover\\b", "Case/ Cover", mod_raw);
        mod_raw <- gsub("\\bCasing/Screen\\b", "Casing/ Screen", mod_raw);        
#         mod_raw <- gsub(" carefully ", " careful ", mod_raw, ignore.case=TRUE);        
#         mod_raw <- gsub("\\bchargers\\b", "charger", mod_raw, ignore.case=FALSE);        
        mod_raw <- gsub("\\bchip/crack\\b", "chip/ crack", mod_raw);        
#         mod_raw <- gsub("\\bchips\\b", "chip", mod_raw, ignore.case=FALSE);
        mod_raw <- gsub("\\bCLEAN\\!LIKE\\b", "CLEAN! LIKE", mod_raw);        
#         mod_raw <- gsub("\\bcleanly\\b", "clean", mod_raw, ignore.case=FALSE);        
#         mod_raw <- gsub("\\b(C|c)olor(.*)s\\b", "\\1olor", mod_raw, ignore.case=FALSE);
# #mod_raw <- glb_allobs_df[c(280,1411), txt_var]; mod_raw        
        mod_raw <- gsub("\\bColors,models\\b", "Colors ,models", mod_raw);   
#         mod_raw <- gsub("\\bcompletely\\b", "complete", mod_raw, ignore.case=FALSE);   
# #mod_raw <- glb_allobs_df[c(178), txt_var]; mod_raw        
#
        mod_raw <- gsub("\\bCONDITION..CLEAN\\b", "CONDITION ..CLEAN", mod_raw);
        mod_raw <- gsub("\\bcondition,comes\\b", "condition ,comes", mod_raw);
        mod_raw <- gsub("\\bcondition\\.Device\\b", "condition .Device", mod_raw);
        mod_raw <- gsub("\\bconditionHas\\b", "condition Has", mod_raw);        
        mod_raw <- gsub("\\bcondition\\.\\.\\.like\\b", "condition ...like", mod_raw);    
        mod_raw <- gsub("\\bcondition\\*Minimal\\b", "condition *Minimal", mod_raw);    
        mod_raw <- gsub("\\bCondition-Moderate\\b", "Condition -Moderate", mod_raw);
        mod_raw <- gsub("\\bcondition\\.The\\b", "condition .The", mod_raw);        
        mod_raw <- gsub("\\bCONDITION\\.VERY\\b", "CONDITION .VERY", mod_raw);        
#         mod_raw <- gsub(" (conditon|condtion|contidion|conditions)", " condition", mod_raw,
#         mod_raw <- gsub("\\b(conditon|condtion|contidion|conditions)\\b", "condition", mod_raw,
# ", "\\1\\. \\2", mod_raw,
#                         ignore.case=TRUE);
#         mod_raw <- gsub("(condition)(Has)", "\\1\\. \\2", mod_raw);
#         
#         mod_raw <- gsub("\\bCONNECTED\\b", "CONNECT", mod_raw, ignore.case=FALSE);        
#         mod_raw <- gsub("\\bconnects\\b", "connect", mod_raw, ignore.case=FALSE);        
#         mod_raw <- gsub(" consist ", " consistent ", mod_raw, ignore.case=TRUE);
# #mod_raw <- glb_allobs_df[c(195, 379, 437), txt_var]; mod_raw        
#         mod_raw <- gsub("\\bCosmetics\\b", "Cosmetic", mod_raw, ignore.case=FALSE);        
        mod_raw <- gsub("\\bCracked/Damaged\\b", "Cracked/ Damaged", mod_raw);        
        mod_raw <- gsub("\\bcracksNo\\b", "cracks No", mod_raw);        
#         
#         mod_raw <- gsub("\\b(D|d)amaged\\b", "\\1amage", mod_raw, ignore.case=TRUE);
# #mod_raw <- glb_allobs_df[c(116, 1360), txt_var]; mod_raw        
#         mod_raw <- gsub("\\bDays\\b", "Day", mod_raw, ignore.case=TRUE);        
#         mod_raw <- gsub(" DEFAULTING ", " DEFAULT ", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub("\\bdefect(ive)*\\b", "defects", mod_raw, ignore.case=FALSE);
#         mod_raw <- gsub(" definitely ", " definite ", mod_raw, ignore.case=TRUE);        
#         mod_raw <- gsub("\\b(D|d)ented\\b", "\\1ent", mod_raw, ignore.case=FALSE);    
#         mod_raw <- gsub(" described", " describe", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub(" desciption", " description", mod_raw, ignore.case=TRUE);    
#         mod_raw <- gsub(" devices", " device", mod_raw, ignore.case=TRUE);        
#         mod_raw <- gsub(" Digi\\.", " Digitizer\\.", mod_raw, ignore.case=TRUE);        
#         mod_raw <- gsub("\\b(ding|dinged)\\b", "dings", mod_raw, ignore.case=TRUE);   
#         mod_raw <- gsub(" display\\.New ", " display\\. New ", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub(" displays", " display", mod_raw, ignore.case=TRUE);
        mod_raw <- gsub("\\bdo( +)not\\b", "dont", mod_raw);
        mod_raw <- gsub("\\b(D|d)oes( +)(N|n)(O|o)(T|t)\\b", "\\1oes\\3\\5", mod_raw);
#         mod_raw <- gsub("\\b(drop|drops)\\b", "dropped", mod_raw, ignore.case=TRUE); 
        
#         mod_raw <- gsub("\\b(E|e)dge\\b", "\\1dges", mod_raw, ignore.case=FALSE);        
#         mod_raw <- gsub(" effect ", " affect ", mod_raw, ignore.case=TRUE);        
#         mod_raw <- gsub(" Excellant ", " Excellent ", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub(" excellently", " excellent", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub(" EUC ", " excellent used condition", mod_raw, ignore.case=TRUE);  
#         mod_raw <- gsub(" feels ", " feel ", mod_raw, ignore.case=TRUE);
        mod_raw <- gsub("\\bfineiCloud\\b", "fine iCloud", mod_raw, ignore.case = FALSE);
#         mod_raw <- gsub(" fine.Its ", " fine. Its ", mod_raw, ignore.case=TRUE);       
#         mod_raw <- gsub("\\bfix\\b", "fixed", mod_raw, ignore.case=TRUE);        
#         mod_raw <- gsub("\\bflaws\\b", "flaw", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub("\\bflawlessly\\b", "flawless", mod_raw, ignore.case=TRUE);        
#         mod_raw <- gsub(" Framing ", " Frame ", mod_raw, ignore.case=TRUE);        
#         
#         mod_raw <- gsub(" functioanlity", " functionality", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub("\\bfunction(ing|ality)\\b", "functional", mod_raw, ignore.case=TRUE); 
#         mod_raw <- gsub(" functional\\.Very little ", " functional\\. Very little ", mod_raw,
#                         ignore.case=TRUE); 
        
        mod_raw <- gsub("\\b([[:digit:]]+) (GB|gb)\\b", "\\1\\2", mod_raw);
        mod_raw <- gsub("\\b([[:digit:]]+) gig\\b", "\\1gb", mod_raw);        
        mod_raw <- gsub("\\b(G|g)(EEK|eek) (S|s)(QUAD|quad)\\b", "\\1\\2\\3\\4", mod_raw);
#         mod_raw <- gsub("^Gentle ", "Gently ", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub("\\(gray color", "\\(spacegray color", mod_raw, ignore.case=TRUE); 
#         mod_raw <- gsub(" GREAT\\.SCreen ", " GREAT\\. SCreen ", mod_raw,
#                         ignore.case=TRUE);        
        mod_raw <- gsub("\\bGUARANTEES-IT\\b", "GUARANTEES -IT", mod_raw);
#         mod_raw <- gsub("\\b(guarantee|guarantees)\\b", "guaranteed", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub("\\ba handful of times\\b", "sparingly", mod_raw, ignore.case=TRUE); 
#         mod_raw <- gsub("\\bhardly any\\b", "no", mod_raw, ignore.case=TRUE); 
#         mod_raw <- gsub("\\bhardly ever used\\b", "sparingly used", mod_raw, ignore.case=TRUE);
#         
        mod_raw <- gsub("\\biCL0UD\\b", "iCLOUD", mod_raw);        
        mod_raw <- gsub("\\bI (CLOUD|cloud)\\b", "I\\1", mod_raw);        
#         mod_raw <- gsub("^iPad Black 3rd generation ", "iPad 3 Black ", mod_raw,
#                         ignore.case=TRUE);  
        mod_raw <- gsub("\\bIMEINo\\b", "IMEI No", mod_raw);
        mod_raw <- gsub("\\bIMIE\\b", "IMEI", mod_raw);        
#         mod_raw <- gsub("\\bincluding\\b", "included", mod_raw, ignore.case=FALSE);        
#         mod_raw <- gsub(" install\\. ", " installed\\. ", mod_raw, ignore.case=TRUE);   
#         mod_raw <- gsub("inivisible", "invisible", mod_raw, ignore.case=TRUE);        
        mod_raw <- gsub("\\bI pad\\b", "Ipad", mod_raw);
        mod_raw <- gsub("\\b(I|i)(P|p)(A|a)(D|d) (A|a)(I|i)(R|r)\\b", "\\1\\2\\3\\4\\5\\6\\7",
                        mod_raw); 
        mod_raw <- gsub("\\b(I|i)(P|p)(A|a)(D|d) (M|m)ini\\b", "\\1\\2\\3\\4\\5ini", mod_raw);
        mod_raw <- gsub("\\b(I|i)(P|p)(A|a)(D|d) (M|m)inis\\b", "\\1\\2\\3\\4\\5ini", mod_raw);  
        mod_raw <- gsub("\\b(IPAD|Ipad|iPad|ipad) ([[:digit:]])\\b", "\\1\\2", mod_raw);
        mod_raw <- gsub("\\b(Ipadair|iPadAir|ipadair) ([[:digit:]])\\b", "\\1\\2",
                        mod_raw);
        mod_raw <- gsub("\\b(iPadMini|iPadmini) ([[:digit:]])\\b", "\\1\\2", mod_raw);
        mod_raw <- gsub("\\bI Phone\\b", "IPhone", mod_raw);        
        mod_raw <- gsub("\\bIS-NO\\b", "IS -NO", mod_raw, ignore.case = FALSE)
#
#         mod_raw <- gsub(" Keeped ", " Kept ", mod_raw, ignore.case=TRUE);        
#         mod_raw <- gsub(" knicks ", " nicks ", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub(" lightening ", " lightning ", mod_raw, ignore.case=TRUE);
        mod_raw <- gsub("\\bLightning-to-USB\\b", "Lightning- to- USB", mod_raw);        
        
        mod_raw <- gsub("\\b(L|l)(IKE|ike)( |-)(N|n)(EW|ew)\\b", "\\1\\2\\4\\5", mod_raw);
            #mod_raw <- c("like new", "LIKE new", "like NEW", "LIKE NEW", "blike new", "like-new", "like newb")
        mod_raw <- gsub("\\bLIKENEW!ONE\\b", "LIKENEW! ONE", mod_raw);        
#         mod_raw <- gsub("\\b(lock|locks)\\b", "locked", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub("\\blots\\b", "lot", mod_raw, ignore.case=TRUE);        
#         mod_raw <- gsub(" manuals ", " manual ", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub(" mars ", " marks ", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub(" marks\\.Absolutely ", " marks\\. Absolutely ", mod_raw,
#                         ignore.case=TRUE);        
#         mod_raw <- gsub("\\bmarkings\\b", "marks", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub("\\bminis\\b", "mini", mod_raw, ignore.case=FALSE);           
#         mod_raw <- gsub(" minimum", " minimal", mod_raw, ignore.case=TRUE);        
#         mod_raw <- gsub(" MINT\\.wiped ", " MINT\\. wiped ", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub("\\bmonth\\b", "months", mod_raw, ignore.case=TRUE);
# #mod_raw <- glb_allobs_df[c(1803), txt_var]; mod_raw
        
        mod_raw <- gsub("\\bNew-Other\\b", "New -Other", mod_raw);
#         mod_raw <- gsub(" NEW\\!(SCREEN|ONE) ", " NEW\\! \\1 ", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub(" new looking$", " looks new", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub(" newer ", " new ", mod_raw, ignore.case=TRUE);   
#         mod_raw <- gsub("\\bnoted\\b", "note", mod_raw, ignore.case=TRUE);        
        
#         mod_raw <- gsub(" oped ", " opened ", mod_raw, ignore.case=TRUE);        
#         mod_raw <- gsub(" opening", " opened", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub(" operated", " operational", mod_raw, ignore.case=TRUE);
        mod_raw <- gsub("\\botter box\\b", "otterbox", mod_raw);        
#         
#         mod_raw <- gsub("\\bpackage\\b", "packaging", mod_raw, ignore.case=FALSE);
#         mod_raw <- gsub("\\bPACKAGE\\b", "PACKAGing", mod_raw, ignore.case=FALSE);        
# #mod_raw <- glb_allobs_df[c(360, 1142), txt_var]; mod_raw        
        mod_raw <- gsub("\\bperfectlycord\\b", "perfectly cord", mod_raw);        
#         mod_raw <- gsub(" performance", " performs", mod_raw, ignore.case=TRUE);        
#         mod_raw <- gsub(" personalized ", " personal ", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub("\\bPhysically\\b", "Physical", mod_raw, ignore.case=FALSE);        
#         mod_raw <- gsub("\\b(picture|pictured)\\b", "pictures", mod_raw, ignore.case=FALSE);
#         mod_raw <- gsub("\\bPICTURE\\b", "PICTUREs", mod_raw, ignore.case=FALSE);
# #mod_raw <- glb_allobs_df[c(184, 892), txt_var]; mod_raw
#         mod_raw <- gsub("\\b[P|p]ower(ed|ing|s)\\b", "\\1ower", mod_raw, ignore.case=FALSE);
#         mod_raw <- gsub(" pre- owned ", " used ", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub("\\bprevious\\b", "previously", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub("\\bpreviously (owned|used)\\b", "used", mod_raw, ignore.case=TRUE);
        mod_raw <- gsub("\\b(P|p)reviously(.*)(O|o)wned\\b", "\\1reviously\\3wned\\2",
                        mod_raw); 
        mod_raw <- gsub("\\b(P|p)reviously(.*)(U|u)sed\\b", "\\1reviously\\3sed\\2", mod_raw);
#         mod_raw <- gsub("\\bproblem\\b", "problems", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub(" products ", " product ", mod_raw, ignore.case=TRUE);        
#         mod_raw <- gsub("\\bprotected\\b",  "protector", mod_raw, ignore.case=FALSE);       
#         mod_raw <- gsub("\\bprotection\\b", "protector", mod_raw, ignore.case=FALSE);
#         mod_raw <- gsub("\\bPROTECTION\\b", "PROTECTOR", mod_raw, ignore.case=FALSE);       
        
        mod_raw <- gsub("\\bREADiPad\\b", "READ iPad", mod_raw);
#         mod_raw <- gsub(" re- assemble ", " reassemble ", mod_raw, ignore.case=TRUE);
        mod_raw <- gsub("\\bREFURB\\.", "REFURBished.", mod_raw);
#         mod_raw <- gsub(" reponding", " respond", mod_raw, ignore.case=TRUE);   
        mod_raw <- gsub("\\bright-hand\\b", "right -hand", mod_raw);
#         mod_raw <- gsub(" rotation ", " rotate ", mod_raw, ignore.case=TRUE);   
#         
#         mod_raw <- gsub(" Sales ", " Sale ", mod_raw, ignore.case=TRUE);
        mod_raw <- gsub("\\bScratch-Free\\b", "Scratch- Free", mod_raw);
        mod_raw <- gsub("\\bSCRATCHES/BLEMISHES...SCRATCHES\\b", "SCRATCHES/ BLEMISHES... SCRATCHES", mod_raw);
        mod_raw <- gsub("\\bscratches,clear\\b", "scratches, clear", mod_raw);
        mod_raw <- gsub("\\bScratches/Dent\\b", "Scratches/ Dent", mod_raw);
        mod_raw <- gsub("\\bScratches/scuffs/nicks/scrapes\\b", "Scratches/ scuffs/ nicks/ scrapes", mod_raw);        
#         mod_raw <- gsub(" scratchs ", " scratches ", mod_raw, ignore.case=TRUE);        
#         mod_raw <- gsub("\\b(scratchs|scratching)\\b", "scratches", mod_raw, ignore.case=FALSE);
        mod_raw <- gsub("\\bset up\\b", "setup", mod_raw);        
#         mod_raw <- gsub(" shipped| Shipment", " ship", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub("\\bshowing\\b", "shows", mod_raw, ignore.case=FALSE);        
        mod_raw <- gsub("\\b(shrink|SHRINK) (wrap|WRAP)", "\\1\\2", mod_raw);        
#         mod_raw <- gsub("\\bshuts\\b", "shut", mod_raw, ignore.case=TRUE);        
#         mod_raw <- gsub(" sides ", " side ", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub(" skinned,", " skin,", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub("\\bSlightly\\b", "slight", mod_raw, ignore.case=FALSE);        
        mod_raw <- gsub("\\b(Space|space) (G|g)r(a|e)y\\b", "\\1\\2ray", mod_raw); 
#         mod_raw <- gsub(" spec ", " speck ", mod_raw, ignore.case=TRUE);        
        mod_raw <- gsub("\\bsomescratches\\b", "some scratches", mod_raw);  
#         mod_raw <- gsub(" Sticker ", " Stickers ", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub("\\bstoring", "store", mod_raw, ignore.case=FALSE);        
#         mod_raw <- gsub("SWAPPA\\.COM", "SWAPPAsdotCOM", mod_raw, ignore.case=TRUE);        
#         
#         mod_raw <- gsub(" T- Mobile", "  TMobile", mod_raw, ignore.case=TRUE);  
#         mod_raw <- gsub("\\b(tear|TEAR)(s|S)\\b", "\\1", mod_raw, ignore.case=FALSE);         
# #mod_raw <- glb_allobs_df[c(376), txt_var]; mod_raw        
        mod_raw <- gsub("\\b(touch|Touch|TOUCH) (screen|SCREEN)\\b", "\\1\\2", mod_raw);
#         mod_raw <- gsub("\\bTURN\\b", "TURNS", mod_raw, ignore.case=FALSE);        
#         
        mod_raw <- gsub("\\bUnlockedCracked\\b", "Unlocked Cracked", mod_raw);
#         mod_raw <- gsub("\\bUNUSABLE\\b", "UNUSED", mod_raw, ignore.case=FALSE);         
#         mod_raw <- gsub("\\b(update|updates)\\b", "updated", mod_raw, ignore.case=FALSE);
#         mod_raw <- gsub("\\bupgrade\\b", "upgraded", mod_raw, ignore.case=FALSE);        
#         mod_raw <- gsub(" uppser ", " upper ", mod_raw, ignore.case=TRUE); 
#         mod_raw <- gsub("use*Case\\b", "use *Case", mod_raw, ignore.case = FALSE)    
#         mod_raw <- gsub(" use\\.Scratches ", " use\\. Scratches ", mod_raw,
#                         ignore.case=TRUE);  
#         
#         mod_raw <- gsub(" verify ", " verified ", mod_raw, ignore.case=TRUE);        
#         mod_raw <- gsub(" wear\\.Device ", " wear\\. Device ", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub("\\bwears\\b", "\\wear", mod_raw, ignore.case=TRUE);
# #mod_raw <- glb_allobs_df[c(167, 272), txt_var]; mod_raw        
#         mod_raw <- gsub(" whats ", " what's ", mod_raw, ignore.case=TRUE);
#         mod_raw <- gsub(" WiFi\\+4G ", " WiFi \\+ 4G ", mod_raw, ignore.case=TRUE);
        mod_raw <- gsub("\\b(W|w)(IFI|ifi)( |-)(ONLY|only)\\b", "\\1\\2\\4", mod_raw);
        mod_raw <- gsub("\\bwill( +)not\\b", "wont", mod_raw);  
        
        mod_raw <- gsub("\\byr\\b", "year", mod_raw);         
        mod_raw <- gsub("\\bZa(a|g)g Invisible(.*)Shield\\b", "ZaagInvisibleShield", mod_raw);
### bid._sp
        return(mod_raw) }    
    , args = c("description"))
# To identify glb_id_vars with >=10 obs
#mod_raw <- glb_allobs_df[sel_obs(list(descr.my.contains="\\bdoes( +)not\\b")), glbFeatsText]
#mod_raw <- glb_allobs_df[sel_obs(list(descr.my.contains="\\bipad [[:digit:]]\\b")), glbFeatsText][01:10]
#mod_raw <- glb_allobs_df[sel_obs(list(descr.my.contains="pad mini")), glbFeatsText][11:20]
#mod_raw <- glb_allobs_df[sel_obs(list(descr.my.contains="pad mini")), glbFeatsText][21:30]
#mod_raw <- glb_allobs_df[sel_obs(list(descr.my.contains="pad mini")), glbFeatsText][31:40]

glbFeatsDerive[["prdl.my.fctr"]] <- list(
    mapfn = function(productline, description) { 
        as.factor(gsub(" ", "", productline)) }
    , args = c("productline"))
# glbFeatsDerive[["prdl.descr.my.fctr"]] <- list(
#     mapfn = function(productline, description) { 
#         as.factor(paste(gsub(" ", "", productline), 
#                         as.numeric(nchar(description) > 0), 
#                         sep = "#")) }
#     , args = c("productline", "description"))    
#print(mycreate_sqlxtab_df(glb_allobs_df, c("prdl.descr.my.fctr", "sold")))

#     mapfn=function(startprice) { return(scale(log(startprice))) }    
#     , args=c("startprice"))
#     mapfn=function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
#     mapfn=function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
#     mapfn=function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
#     mapfn=function(Week) { return(substr(Week, 1, 10)) }
#     mapfn=function(raw) { tfr_raw <- as.character(cut(raw, 5)); 
#                           tfr_raw[is.na(tfr_raw)] <- "NA.my";
#                           return(as.factor(tfr_raw)) }
#     , args=c("raw"))
#     mapfn=function(PTS, oppPTS) { return(PTS - oppPTS) }
#     , args=c("PTS", "oppPTS"))

# # If glb_allobs_df is not sorted in the desired manner
#     mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glb_allobs_df)$ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }

# glbFeatsDerive[["<txt_var>.niso8859.log"]] <- list(
#     mapfn=function(<txt_var>) { match_lst <- gregexpr("&#[[:digit:]]{3};", <txt_var>)
#                         match_num_vctr <- unlist(lapply(match_lst, 
#                                                         function(elem) length(elem)))
#                         return(log(1 + match_num_vctr)) }
#     , args=c("<txt_var>"))

#     mapfn=function(raw) { mod_raw <- raw;
#         mod_raw <- gsub("&#[[:digit:]]{3};", " ", mod_raw);
#         # Modifications for this exercise only
#         mod_raw <- gsub("\\bgoodIn ", "good In", mod_raw);
#                           return(mod_raw)

#         # Create user-specified pattern vectors 
# #sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
#         if (txt_var %in% c("Snippet", "Abstract")) {
#             txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
#                 as.integer(0 + mycount_pattern_occ("Metropolitan Diary:", 
#                                                    glb_allobs_df[, txt_var]))
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])

# glb_allobs_df$<descriptor>.my <-
#     plyr::revalue(glb_allobs_df$<descriptor>.my, c(
#         "ABANDONED BUILDING" = "OTHER",
#         "##"                      = "##"
#     ))
# print(<descriptor>_freq_df <- mycreate_sqlxtab_df(glb_allobs_df, c("<descriptor>.my")))
# # print(dplyr::filter(<descriptor>_freq_df, grepl("(MEDICAL|DENTAL|OFFICE)", <descriptor>.my)))
# # print(dplyr::filter(dplyr::select(glb_allobs_df, -<var.zoo>), 
# #                     grepl("STORE", <descriptor>.my)))

# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]

glb_derive_vars <- names(glbFeatsDerive)
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glb_allobs_df[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst))); 
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]); 

glb_date_vars <- NULL # or c("<date_var>")
glb_date_fmts <- list(); #glb_date_fmts[["<date_var>"]] <- "%m/%e/%y"
glb_date_tzs <- list();  #glb_date_tzs[["<date_var>"]] <- "America/New_York"
#grep("America/New", OlsonNames(), value=TRUE)

glbFeatsPrice <- c("startprice") #: bid._sp # NULL or c("<price_var>")

# Text Processing Step: custom modifications not present in txt_munge
glbFeatsText <- c("descr.my")   # NULL # 
Sys.setlocale("LC_ALL", "C") # For english
## [1] "C/C/C/C/C/en_US.UTF-8"
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "ebay_mytxt_"

# Text Processing Step: tolower
# Text Processing Step: removePunctuation (use custom transformer to replace with space ???)
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words; Check stemming of "significant" words - any stopped words that should be stemmed with them ?
if (!is.null(glbFeatsText)) {
    require(tm)

    glb_txt_stop_words[["descr.my"]] <- sort(c(NULL    
### bid._sp    
#         , setdiff(removePunctuation(stopwords("english")), "no")                                
#         ,"ac"
#         # cor.y.train == NA
#         ,unlist(strsplit(paste(c(NULL
#         ,"128gb,1st,32gb,3g,64gb,90,acceptable,activation,amount,average,bad,buttons,buy,came,camera,can,care,carrier"
#         #,casing 
#         ,"certified,charge,charging,cleaned,clear,come,components,contain,corner,correctly,covered,customer,earbuds"
#         ,"engraved,engraving,engravement" # somehow didn't show up in the cor.y.train == NA list
#         ,"entire,except,fair,features,feel,fine,generation,get,gift,got,heavily,heavy,however,imei,include,inspected,invisible,invisibleshield"
#         ,"ipad,ipads"
#         ,"issues"
#         #,items,
#         ,"keyboard,lightning,listing,little,looks,lower"
#         ,"manufacture,manufacturer"# somehow didn't show up in the cor.y.train == NA list
#         ,"meaning,model,near,need,needs,nicks,opened,operational,otherwise"
#         ,"person,personal"# somehow didn't show up in the cor.y.train == NA list
#         ,"phone,photos,pics,plastic,port,professionally"
#         ,"purchased,purchasing"# somehow didn't show up in the cor.y.train == NA list
#         ,"quality,questions,read,ready"
#         ,"receive,received"# somehow didn't show up in the cor.y.train == NA list
#         ,"removed,replaced,retail,return,returns,runs"
#         #,scratch,
#         ,"scuffing,sealed,sell,seller,selling,shape,ship,shown,silver,since,sold,sound,spacegray,stock,sync,tablet,taken,technician,tests,third,time,touch,units,unlocked,week,wifi,without"
#         ,"wrap" # somehow didn't show up in the cor.y.train == NA list
#         ,"zagg"
#         ), collapse=",")
#         , "[,]")) #err.abs.fit.sum=26.869473 w/o items,scratch
#         
#         # cor.y.abs is low
#         #,"always","comes","grade","moderate","protector"
### bid._sp
### !_sp
            # freq == 1; keep "gold"
            ,"09","17","28","34","360","5c","5point1point1","511","6428"
                ,"7point9","7point9in","79in"
                ,"8point5","8point25","82510"
                ,"910","9510","9point7","9point75","97510","99"
            ,"a1314","a1430","abused","accept","accounts","across","actuuly","add","advised"
                ,"affects","am","ans","antenna","anti","anyone","anything","applied","applying"
                ,"area","arizona","att","attached"
            ,"backlight","backlit","backplate","barley"
                ,"beetle","beginning","bend","besides","between"
                ,"bidder","binder","bonus","book","boot","bound","brick","broke","bruises"
                ,"buyers"
            ,"capacity","causing","cherished","chrome","classes","closely"
                ,"condtion","conditon","confidence","considerable","consist","consistent"
                    ,"consumer","contents","contidion","control"
                    ,"couldnt"
                ,"cream","customize","cuts"
            ,"daily","date","daughter"
                ,"deactivated","decent","deep","defender","defense","degree","demonstration"
                    ,"depicted","depress","desciption"
                ,"difficulty","digi","disclaimer","discoloration","distressed","divider"
                ,"dlxnqat9g5wt","dock","documents","done","durable","dust","duty"
            ,"each","effect","either","emblem","erased","esi","essentially","etch","etched"
                ,"euc","every","exact","exhibition","expires"
            ,"facing","faded","faint","february","film","final","five","flickers"
                ,"folding","forgot","forwarders"
                ,"freezes","freight"
                ,"functioanlity"
            ,"games","generic","genuine","glitter","goes","gray","grey","guide"
            ,"hairline","half","handstand","hdmi","here","high","higher"
                ,"hold","hole","hospital"
            ,"immaculate","impact"
                ,"inivisible","instead"
                    ,"intended","interest","interior","international","internationally"
                        ,"into","intro"
                ,"ios","isnt","itself","ive"
            ,"jack","july"
            ,"keyword","kids","kind","knicks"
            ,"l","largest","last","late","length","let","letters","level"
                ,"lifting","limited","literally","literature"
                ,"local","logic","long","longer","looping","loose","loss","lost"
            ,"mars"
                ,"mb292ll","mc707ll","mc916ll","mc991ll","md789ll","mf432ll"
                ,"mic","middle","mind","minimum","mixed"
                ,"myself"
            ,"neither","newer","non","none","nonviewing","nor","november"
            ,"occasional","oem","often","online","oped","outside","over"
            ,"padfolio","pairing","paperwork","past","period","pet","piece"
                ,"plate","played","plug"
                ,"poor","portfolio","portion","pouch"
                ,"preinstalled","pressure","price","proof","provided"
            ,"ranging","rather"
                ,"real","realized","reassemble","reboot","receipt","recently","red"
                    ,"reflected","refunds","relisting","remote","repeat","reponding"
                    ,"required"
                    ,"reserve","reshaped","residue","respond","restarts","result"
                    ,"reviewed"
                ,"ringer","roughly","rubber"
            ,"said","same","school","scratchs","screeb"
                ,"seamlessly","seem","seen","semi","send","september","serious"
                ,"shell","shipment","short","showroom"
                ,"sighs","site","size"
                ,"sleeve","slice"
                ,"smoke","smooth","smudge","sn"
                ,"softer","software","somewhat","soon"
                ,"space","sparingly","sparkiling","spec","special","speck","speed","speigen"
                ,"stains","standup","start","status","stopped","strictly"
                ,"subtle","sustained"
                ,"swappadotcom","swiped","swivel"
            ,"take","technical","tempered","texture"
                ,"thank","then","therefore","think","those","though"
                ,"toddler","topback","totally","touchy","toys"
                ,"tried","turn","typical"
            ,"u","university","unknown","untouched","uppser"
            ,"valid","vary","viewing","virtually"
            ,"want","wavy","website","whole","why","winning","worn"
            ,"zaag","zero","zombie","zoogue"

            # cor.y.train == NA
            ,"account","applecare","download","expect","fourth","greeting","maybe"
            ,"plus","purposes","significant","title","volume"

            # chisq.pval high (e.g. == 1); 
            #   keep
            #       carrier.fctr:  "sprint", "verizon"
            #       cellular.fctr:  "3g", "4g",wifion
            #       color.fctr:     "gold"
            #       prdl.descr.my.fctr: 
            #       storage.fctr:   "128gb"

            ,"2016"
            ,"acceptable","actual","amount","awesome"
            ,"beautiful","before","bent","best","blocked","blocks"
            ,"capable","converted"
            ,"find"
            ,"gift"
            ,"handled","handling","headphone"
            ,"im","information"
            ,"love"
            ,"march","meaning","means","medium","money"
            ,"necessary"
            ,"offer","once"
            ,"page","product","products"
            ,"second","seconds","should","silver","skin","skinned"
            ,"tape","thoroughly","twice"
            ,"user"
            ,"way","which"

            # nzv.freqRatio high (e.g. >= glb_nzv_freqCut); 
            #   keep
            #       carrier.fctr:       "sprint", "tmobile", "verizon"
            #       cellular.fctr:      "3g", "4g",wifionly
            #       color.fctr:         "gold",spacegray
            #       condition.fctr:     
            #           levels:
            #   "Used", "For parts or not working", "Manufacturer refurbished", "New", "New other (see details)", "Seller refurbished"     
            #           stemmed tokens:
            #   manufactur                    
            #       prdl.descr.my.fctr: "ipad1",ipad3,ipad4,ipadair2,ipadmini2
            #       storage.fctr:       "128gb"

            ,"14","2015","3","30","4","5","6","7","8","9","90","9point5","9point7in"
            ,"a1432","able","about","ac","activate","activated","activation"
                ,"additional","adult"
                ,"after"
                ,"ago"
                ,"air"
                ,"along","already","also"
                ,"another","answer"
                ,"appears","approved","april"
                ,"around"
                ,"asis","associated"
                ,"auction"
            ,"backside","bad","battery","because","bestbuy","bezel","blue","bluetooth"
                ,"board","body","both","bottom","bought"
                ,"bright"
                ,"bumps","buy","buying"
            ,"came","camera","cameras","cannot","cant","card","carrier","cellular"
                ,"changed","changing","check","chip","chips"
                ,"color","colors","company","complete","completely","components"
                    ,"connect","connected","connector","connects","contain","contains"
                    ,"corporate","correctly","couple"
                ,"customer"
            ,"data"
                ,"dead"
                    ,"default","defaulting","definite","definitely"
                    ,"delivered","demo","describe","described","details"
                ,"do","does","dont","down"
                ,"drop","dropped","drops"
                ,"due"
            ,"earbuds","easily","ebay","else","engraved","engravement","engraving","entire"
                ,"etc"
                ,"even","ever","everything"
                ,"except","exterior","extremely"
            ,"fantastic","fast","faulty","features","feel","feels","fine","fix","fixed"
                ,"flaw","flaws"
                ,"frame","framing"
            ,"geeksquad","general","get","got","guarantee","guaranteed","guarantees"
            ,"hand","handful","handset","happened","hard","hardly","heavy","home","however"
            ,"id","if","images","imperfections"
                ,"inside","inspected","install","installed","instructions","invisible"
                ,"iphone","iphones"
                ,"issue","issues"
                ,"itunes"
            ,"keyboard","know","known"
            ,"latest","lcd","least","leather","less"
                ,"life","lightening","lightning","like","line","lining"
                    ,"liquid","liquidation"
                ,"logo","lot","lots","lower"
            ,"magnetic","make","manual","manuals","many","me","memory","missing"
                ,"model","models","moderate","more","most","mostly"
                ,"multiple","musthave"
                ,"my"
            ,"name","network","networks","nice","nick","nicks"
                ,"nonfunctioning","noticeable","now"
            ,"off","old","operated","operational","otherwise","outer","own","owned"
            ,"party","passcode","password"
                ,"performance","performs","person","personal","personalized"
                ,"phone","physical","physically"
                ,"pin","pixels"
                ,"placed","places"
                ,"port","possible"
                ,"pretty","pristine","problem","problems","properly"
                ,"purchase","purchased","purchasing"
            ,"quality","question","questions"
            ,"rarely"
                ,"reactivated","really","rear","receive","received","regular","removed"
                    ,"repair","retail","retina"
                ,"rotate","rotation"
                ,"running","runs"
            ,"s","sale","sales","sameday","scrapes","scroll","setup","several"
                ,"shattered","shrinkwrap","shut","shuts"
                ,"side","sides","sim","single"
                ,"so","something","sometimes","sound"
                ,"speaker","specifics","spent"
                ,"sticker","stickers","storage","store","storing","stuck","stylus"
                ,"super","supply","sure","surface"
                ,"sync"
            ,"taken","technician","than","these","they","thin","third","three"
                ,"till","tiny"
                ,"too","took","touch","touching","touchscreen"
                ,"turns"
                ,"two"
            ,"unable","under","unnoticeable","unopened","unsealed","until"
                        ,"unused","unusable"
                ,"up","update","updated","updates","upgrade","upgraded","upper"
                ,"usage","usually"
            ,"verified","verify"
            ,"wall","water","we","week","weeks", "were","what","whats","where","while"
                ,"wiped","without"
                ,"would"
                ,"wrap","wrapped","wrong"
            ,"x"
            ,"year","years","your"
            ,"zagg","zaaginvisibleshield"
#
### !_sp
                                            ))
}
## Loading required package: tm
## Loading required package: NLP
## 
## Attaching package: 'NLP'
## 
## The following object is masked from 'package:ggplot2':
## 
##     annotate
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txt_var]][grep("^2", glb_post_stem_words_terms_df_lst[[txt_var]]$term), ])
#glb_allobs_df[glb_post_stem_words_terms_mtrx_lst[[txt_var]][, 6] > 0, glbFeatsText]

# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txt_var]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txt_var]], freq <= 2)$term), collapse = ",")

# To identify terms with a specific freq & 
#   are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txt_var]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")

#print(subset(glb_post_stem_words_terms_df_lst[[txt_var]], (freq <= 2)))
#glb_allobs_df[which(terms_mtrx[, 229] > 0), glbFeatsText]

# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txt_var]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txt_var]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txt_var]], is.na(cor.y)))

# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txt_var]], !is.na(cor.y))), 5)

# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txt_var]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txt_var]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txt_var]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txt_var]]), 5)
#glb_allobs_df[glb_post_stem_words_terms_mtrx_lst[[txt_var]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txt_var]][grep("^m", glb_post_stem_words_terms_df_lst[[txt_var]]$term), ])

# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txt_var]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txt_var]], (nzv.freqRatio >= glb_nzv_freqCut) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")

# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txt_var]]), 20)
#mydsp_obs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glb_allobs_df[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glb_category_var, "storage", txt_var)]

# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txt_var]][grep("^moder", glb_post_stop_words_terms_df_lst[[txt_var]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txt_var]][grep("^came$", glb_post_stop_words_terms_df_lst[[txt_var]]$term), ]
# 
# cor(glb_post_stop_words_terms_mtrx_lst[[txt_var]][glb_allobs_df$.lcn == "Fit", term_row_df$pos], glb_trnobs_df[, glb_rsp_var], use="pairwise.complete.obs")

# To identify which stopped words are "close" to a txt term
#sort(cluster_vars)

# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txt_var]][grep("condit", glb_post_stop_words_terms_df_lst[[txt_var]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txt_var]][grep("^con", glb_post_stem_words_terms_df_lst[[txt_var]]$term), ])
#glb_allobs_df[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glb_id_var, "productline", txt_var)]
#glb_allobs_df[which(TfIdf_stem_mtrx[, 191] > 0), c(glb_id_var, glb_category_var, txt_var)]
#which(glb_allobs_df$UniqueID %in% c(11915, 11926, 12198))

# Text Processing Step: mycombineSynonyms
#   To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txt_var]], "not", 0.05)
#   To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txt_var]][grep("^c", glb_post_stem_words_terms_df_lst[[txt_var]]$term), ])
chk_comb_cor <- function(syn_lst) {
#     cor(terms_stem_mtrx[glb_allobs_df$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glb_trnobs_df[, glb_rsp_var], use="pairwise.complete.obs")
    print(subset(glb_post_stem_words_terms_df_lst[[txt_var]], term %in% syn_lst$syns))
    print(subset(get_corpus_terms(tm_map(glb_txt_corpus_lst[[txt_var]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
#     cor(terms_stop_mtrx[glb_allobs_df$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glb_trnobs_df[, glb_rsp_var], use="pairwise.complete.obs")
#     cor(rowSums(terms_stop_mtrx[glb_allobs_df$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glb_trnobs_df[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl",  syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag",  syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent",  syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use",  syns=c("use", "usag")))

glb_txt_synonyms <- list()
glb_txt_synonyms[["descr.my"]] <- #NULL : default
    list(NULL
### bid._sp
#     , list(word="cabl",  syns=c("cabl", "cord"))#err.abs.fit.sum=26.863220  
# #     , list(word="charger",  syns=c("charg", "charger"))         
# #     , list(word="come",  syns=c("came", "come")) 
# #     , list(word="dent",  syns=c("dent", "ding")) 
# #     , list(word="damag",  syns=c(#"bad", "blemish", "broken", "crack", 
# #                                  #defect has +ve cor, others have -ve cor
# #                                  "damag", "dent", "ding",
# #                                  #"scratch", "scuff", "tear", "wear",
# #                                  NULL)) 
# #     # combining damag with defect & dent results in higher err.abs.fit.sum=26.885899
# #     # combining defect with dent in higher err.abs.fit.sum=26.894976    
# #     , list(word="defect",  syns=c(#"bad", "blemish", "broken", "crack", 
# #                     "defect", "dent", #"ding", ding has -ve cor, others have +ve cor 
# #                                  #"scratch", "scuff", "tear", "wear",
# #                                  NULL)) 
#     #, list(word="new", syns=c("brand")) ???
# #     , list(word="scuff",  syns=c("scuf", "scuff"))
# #     , list(word="show",   syns=c("show", "shown"))
# #     , list(word="tablet", syns=c("tab", "tablet"))
### bid._sp    
### !_sp
        ,list(word = "cabl", syns = c("cabl", "cord"))
        ,list(word = "cant", syns = c("cant", "cannot"))
        ,list(word = "descript", syns = c("descript", "discript"))
        ,list(word = "generat", syns = c("gen", "generat"))
        ,list(word = "ipadmini", syns = c("ipadmini", "mini"))
        ,list(word = "kept", syns = c("keep", "kept"))
        ,list(word = "know", syns = c("know", "known"))
        ,list(word = "lightn", syns = c("lighten", "lightn"))
        ,list(word = "passcod", syns = c("passcod", "password"))
        ,list(word = "photo", syns = c("photo", "photograph", "photos", "pic", "pictur"))
        ,list(word = "preown", syns = c("pre", "preown", "previous",
                                        "previouslyus", "previouslyown"))
        ,list(word = "protector", syns = c("protect", "protector"))
        ,list(word = "scuff",  syns = c("scuf", "scuff"))
        ,list(word = "tablet", syns = c("tab", "tablet"))
        ,list(word = "with", syns = c("w", "with"))
        ,list(word = "zagg", syns = c("zagg", "zaaginvisibleshield"))
### !_sp
    )
if (length(glb_txt_synonyms) > 0) names(glb_txt_synonyms) <- glbFeatsText

# Text Processing Step: filterTerms
if (!is.null(glbFeatsText)) {
    require(tm)
    
    # options include: weightTf, myweightTflog1p, myweightTfsqrt, weightTfIdf, weightBM25
    glb_txt_terms_control <- list(weighting = weightTfIdf # : default
                    # termFreq selection criteria across obs: default: list(global=c(1, Inf))
                        , bounds = list(global = c(1, Inf)) # bid._sp: list(global=c(3, Inf)) 
                    # default: c(3, Inf)
                        , wordLengths = c(1, Inf) # bid._sp: c(2, Inf)
                                  ) 
}
glb_txt_cor_var <- glb_rsp_var # bid._sp: "startprice.log10.cut.fctr" # default: glb_rsp_var
# select one from c("union.top.val.cor", "top.cor", default: "top.val", "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq" 
glbFeatsTextTermsMax <- c(15) # bid._sp: c(20) # c(50) in (old) !_sp # default: rep(10, length(glbFeatsText))
names(glbFeatsTextTermsMax) <- glbFeatsText

# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- c(1.0) #bid._sp: c(0.4) #(old) !_sp: 0.2 #default: rep(1, length(glbFeatsText)) 
names(glbFeatsTextAssocCor) <- glbFeatsText

# Text Processing Step: extractPatterns (ngrams)
# Potential Enhancements
#   "Seller refurbished" -> D.P.refurbished.seller ?
#   "Like new" -> D.P.new.like ?
#   "No scratches" -> D.P.scratch.no ?
glb_important_terms <- list()
# Remember to use stemmed terms 

# Have to set it even if it is not used
glb_sprs_thresholds <- c(0.950) # Generates 8 terms
# Properties:
#   numrows(glb_feats_df) << numrows(glb_fitobs_df)
#   Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
#       numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
names(glb_sprs_thresholds) <- glbFeatsText

if (glb_rsp_var_raw != glb_rsp_var)
    glbFeatsExclude <- union(glbFeatsExclude, 
                                            glb_rsp_var_raw)

glbFctrMaxUniqVals <- 23 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer

glb_cluster <- TRUE # bid._sp:TRUE # default:FALSE 
glb_cluster.seed <- 189 # or any integer
### !_sp
glb_cluster_entropy_var <- glb_rsp_var # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsTextClusterVarsExclude <- FALSE # default FALSE
### bid._sp
# glb_cluster_entropy_var <- "sold" #"startprice.log10.cut.fctr" 
# glbFeatsTextClusterVarsExclude <- TRUE # default FALSE
### bid._sp

glb_interaction_only_feats_lst <- list()
### bid._sp
glb_interaction_only_feats_lst[["carrier.fctr"]] <- "cellular.fctr"

glb_nzv_freqCut <- 19 # 19 is caret default
glb_nzv_uniqueCut <- 10 # 4 : bid._sp # 10 : caret::default
### bid._sp

glb_rfe_fit_sizes <- NULL 
#    c(106, 111, 116, 120, 128) # bid0_sp
#    c(8, 11, 16, 21, 32, 64, 128) # bid1_sp
#    c(47,48,49,50,51,52,53,54) # no_sp
### bid1_nosp
    # rfFuncs
    #c(4, 8, 16, 32, 64, 82) # Accuracy@82 = 0.8317
    #c(4, 8, 16, 32, 64, 73, 82, 91) # Accuracy@82 = 0.8325
    #c(16, 32, 64, 73, 76, 78, 80, 81, 82, 83) # Accuracy@82 = 0.8333
    # ldaFuncs
    #c(8, 16, 32, 64, 67) # Accuracy@16 = 0.8519
    #c(8, 12, 16, 20, 32, 64, 67) # Accuracy@12 = 0.8519
    #c(8, 10, 12, 13, 14, 15, 16, 20, 67) # Accuracy@14 = 0.8526
### bid1_nosp
### bid0_nosp
    #c(4, 8, 12, 16, 20, 32, 64, 87) # Accuracy@16 = 0.7877
    #c(12, 14, 15, 16, 17, 18, 20, 87) # Accuracy@17 = 0.7894
    #c(8, 12, 13, 14, 15, 16, 18, 20, 87) # Accuracy@14 = 0.7900 before removing outliers
    #c(8, 12, 13, 14, 15, 16, 18, 20, 87) # Accuracy@15 = 0.7868
### bid0_nosp

# outliers identified by car::outlierTest
glbObsFitOutliers <- list()
### bid._sp
#     c(NULL # default: NULL 
            # biddable == 0 & 1;      err.abs.fit.sum=423.55172
#             #   outliers
#     , 10813 # next  665 w/ rstudent=-5.091080; biddable=3.263257; err.abs.fit.sum=418.598755
#     , 10666 # next 1727 w/ rstudent=-5.163517; biddable=4.293465; err.abs.fit.sum=414.093609
#     , 11736 # next  780 w/ rstudent=-5.181343; biddable=5.670483; err.abs.fit.sum=401.817992
#     # old biddable importance above this
#     , 10781 # next 1323 w/ rstudent=-5.151062; biddable=13.30602; err.abs.fit.sum=396.393721
#     #, 10091 # next 91   w/ rstudent=-4.444452; biddable=; err.abs.fit.sum=402.673715 (up)    
#     #, 10166 # next 560  w/ rstudent=-5.006795; biddable=; err.abs.fit.sum=401.759324 (up)
#     #, 10281 # next 281 w/ rstudent=-4.245087; biddable=; err.abs.fit.sum=401.316926  (up)       
#     #, 10285 # next 285  w/ rstudent=-4.483190; biddable=; err.abs.fit.sum=402.608936 (up)    
#     #, 10446 # next 445  w/ rstudent=-4.663418; biddable=; err.abs.fit.sum=403.074523 (up)
#     #, 10542 # next 1323 w/ rstudent=-5.214517; biddable=; err.abs.fit.sum=401.04205  (up)
#     #, 10543 # next 1323 w/ rstudent=-5.214517; biddable=; err.abs.fit.sum=401.04205  (up)    
#     #, 10561 # next 542  w/ rstudent=-4.736154; biddable=; err.abs.fit.sum=401.56198  (up)    
#     #, 10631 # next 166  w/ rstudent=-5.073048; biddable=; err.abs.fit.sum=401.556788 (up)    
#     #, 11330 # next 630  w/ rstudent=-5.117659; biddable=; err.abs.fit.sum=401.732597 (up)
#     , 10091, 10166, 10281, 10285, 10446, 10542, 10543, 10561, 10631, 11330
#                 # biddable=18.93923; err.abs.fit.sum=359.388769    
#     , 10330 #biddable=19.06084; err.abs.fit.sum=355.895702
#     , 10402 #biddable= 0.0    ; err.abs.fit.sum=351.315181
#     , 10438 #biddable= 0.0    ; err.abs.fit.sum=347.821527
#     , 10624 #biddable= 0.0    ; err.abs.fit.sum=343.724904
#     , 10659 #biddable= 0.0    ; err.abs.fit.sum=331.873603
#     , 11323 #biddable=10.45901; err.abs.fit.sum=324.929562
#     , 11422 #biddable= 0.0    ; err.abs.fit.sum=334.839805 (up)
    
#             biddable == 0;      err.abs.fit.sum=26.713317
#                 , 11448 # outliers; next is 858 w/ rstudent=-5.855132; err.abs.fit.sum=24.212800
#                 , 11583 # outliers; next is 856 w/ rstudent=-4.792849; err.abs.fit.sum=22.164035
#                 , 11581 # outliers; next is 743 w/ rstudent=-4.005054; err.abs.fit.sum=18.842901
#                 , 10837 # outliers; next is 336 w/ rstudent=-5.279215; err.abs.fit.sum=18.124560
#                 , 11442 # outliers; next is 904 w/ rstudent=-4.474844; err.abs.fit.sum=15.533211
#                 , 11697 # outliers; next is 874 w/ rstudent=-3.678664; err.abs.fit.sum=13.829375
#                 , 10799 # .hatvalues == 1; total 8; iPadmini#1; err.abs.fit.sum=13.807283
#                 , 10017 # .hatvalues == 1; total 7; iPad3#1; err.abs.fit.sum=14.620782 (up)
#             , 10027, 10859 # .hatvalues == 1; total 7; iPad1#1; err.abs.fit.sum=14.570246 (up)
#                 , 10332 # .hatvalues == 1; total 7; iPad4#1; err.abs.fit.sum=13.706467
#                 , 11759 # .hatvalues == 1; total 6; iPadAir2#1; err.abs.fit.sum=13.643043
#                 , 10675 # .hatvalues == 1; total 5; iPadAir#1; err.abs.fit.sum=13.623787
#                 , 11119 # .hatvalues == 1; total 4; iPadmini3#1; err.abs.fit.sum=NA
#     , 10017, 10027, 10859 # .hatvalues == 1; total 1; iPad3#1 & iPad1#1; err.abs.fit.sum=13.438903
            
            # biddable == 1;      err.abs.fit.sum=361.78243
#                 , 10813 # outliers; next is 665 w/ rstudent=-5.021180; err.abs.fit.sum=356.83424
#                 , 10666 # outliers; next is 808 w/ rstudent=-4.764126; err.abs.fit.sum=352.46437
#                 , 11736 # outliers; next is 665 w/ rstudent=-4.614022; err.abs.fit.sum=348.59977
#                 , 10542 # outliers; next is 665 w/ rstudent=-4.654923; err.abs.fit.sum=344.18546
#                 , 11330 # outliers; next is 327 w/ rstudent=-4.628972; err.abs.fit.sum=336.12636
#                 , 10561 # outliers; next is 56  w/ rstudent=-4.612970; err.abs.fit.sum=329.50309
#                 , 10166 # outliers; next is 318 w/ rstudent=-4.717238; err.abs.fit.sum=318.50562
#                 , 10543 # outliers; next is 464 w/ rstudent=-4.811116; err.abs.fit.sum=314.32801
#                 , 10285 # outliers; next is 21  w/ rstudent=-4.850822; err.abs.fit.sum=310.19008
#         #, 10091 # outliers; next is 464 w/ rstudent=-4.941448; err.abs.fit.sum=312.94069 (up)
#         #, 10781 # outliers; next is 250 w/ rstudent=-4.793502; err.abs.fit.sum=313.03867 (up)
#                 , 10446 # outliers; next is 371  w/ rstudent=-4.787578; err.abs.fit.sum=307.15681
#                 , 10631 # outliers; next is 165  w/ rstudent=-4.130356; err.abs.fit.sum=303.34549
#         #, 10330 # outliers; next is 217 w/ rstudent=-4.067684; err.abs.fit.sum=312.75121 (up)
#         #, 10402 # outliers; next is 388 w/ rstudent=-4.067684; err.abs.fit.sum=311.84516 (up)
#         #, 10659 # outliers; next is 128 w/ rstudent=-3.982911; err.abs.fit.sum=311.84516 (up)
#         , 10091, 10781, 10330, 10402, 10659#, 10281 outliers; err.abs.fit.sum=282.381827; iPad4#0=13.806011; iPad4#1=7.799398
#         #, 10281 # outliers; next is NA  w/ rstudent=NA;        err.abs.fit.sum=287.147331 (up); iPad4#0=14.372770; iPad4#1=4.591408
#         #, 10624 # outliers; ignored along with 10281        err.abs.fit.sum=289.116467 (up); iPad4#0=; iPad4#1=
#         #, 10624 # outliers; ignored w/o 10281        err.abs.fit.sum=286.415040 (up); iPad4#0=; iPad4#1=
#                 #, 10636 # hatvalues==1; next is 11652; err.abs.fit.sum=290.50254 (up)
#                 , 11652 # hatvalues==1; next is 10636; err.abs.fit.sum=282.183867
#         #err.abs.fit.sum=282.227249
# )
### bid._sp
### no_sp
#     # .hatvalues == 1
#     c(10096, 11062, 11233, 11660, 11768)
### no_sp
### bid0_nosp
# glbObsFitOutliers[["RFE.X"]] <- c(NULL
#     # is.na(RFE.X.glm$.dffits)
#     , 10026, 11172, 11357, 11767
# )
# glbObsFitOutliers[["CSM.X"]] <- c(NULL
#     # is.na(.dffits) 
#     , 10045, 10089, 10918, 11852    # iPad2#1
#     , 10852, 11340, 11464   # iPadmini3#0
#     , 10014, 10025, 10218, 10287, 10382, 10789, 11005, 11404, 11497, 11580, 11763, 11835 # c("Unknown#0", "iPad1#0", "iPad3#0", "iPadAir#0", "iPadAir2#0", "iPadmini#0")
#     , 10155, 10362, 10746, 11029, 11451, 11724, 11759, 11799, 11807 # c("Unknown#1", "iPad1#1", "iPad2#0", "iPad3#1", "iPad4#0", "iPadAir#1", "iPadAir2#1", "iPadmini#1", "iPadmini3#1")
#     , 11001 # Unknown#1
#     
#     # min(.rstudent) <= -121446236
#     , 11760 # iPadAir2#0
#     , 11751 # iPad2#1
#     # max(.rstudent) >=   70875386
#     , 11817 # Unknown#1
#     , 10763 # iPad1#0
#     , 10875 # iPad1#1
#     
#     # .hatvalues >= 0.99
# )
#     c(NULL
#         # is.na(CSM.X.glm$.dffits)
#         ,10026, 10089, 10218, 10382, 10918, 11005
#         # CSM.X.glm$.hatvalues > 0.99
#         ,10014, 11444, 11448, 11455, 11835, 11852
#         # CSM.X.glm$.rstudent <= -43969313 & >= 44111144
#         ,10675, 11272
#     )
### bid0_nosp

# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#print(outliers <- car::outlierTest(glb_models_lst[["CSM.X.glm"]]$finalModel))
#print(outliers_df <- data.frame(.Bonf.p=outliers$bonf.p))

#mdlId <- "CSM.X.glm"; mdlIdFamily <- paste0(head(unlist(str_split(mdlId, "\\.")), -1), collapse="."); obs_df <- dplyr::filter_(glb_fitobs_df, interp(~(!(var %in% glbObsFitOutliers[[mdlIdFamily]])), var = as.name(glb_id_var))); model_diags_df <- cbind(obs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[[mdlId]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[[mdlId]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[[mdlId]]$finalModel)));print(summary(model_diags_df[, c(".rstudent",".dffits",".hatvalues")]))

#print(subset(model_diags_df, is.na(.dffits))[, glb_id_var])
#print(model_diags_df[which.min(model_diags_df$.dffits), ])
#print(subset(model_diags_df, .hatvalues > 0.99))
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glb_fitobs_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))

#indep_vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(glb_sel_mdl_id, ".importance"))), glb_featsimp_df))); indep_vars <- indep_vars[!grepl(".fctr", indep_vars, fixed=TRUE)]

#myplot_parcoord(obs_df=model_diags_df[, c(glb_id_var, glb_category_var, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:20])], obs_ix=row.names(model_diags_df) %in% names(outliers$rstudent)[1], id_var=glb_id_var, category_var=glb_category_var)
#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glb_category_var] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glb_id_var, glb_category_var, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glb_id_var, category_var=glb_category_var)
#table(glb_fitobs_df[model_diags_df[, glb_category_var] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glb_fitobs_df[model_diags_df[, glb_category_var] %in% c("iPad1#1"), c(glb_id_var, "startprice")]

# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glb_id_var, glb_category_var, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glb_id_var, category_var=glb_category_var)

#dffits_ctgry_df <- subset(dffits_df, prdl.descr.my.fctr %in% c("Unknown#0"))
#myplot_parcoord(obs_df=dffits_ctgry_df[, c(glb_id_var, glb_category_var, ".dffits", ".Bonf.p", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:5])], obs_ix=seq(1:nrow(dffits_ctgry_df))[!is.na(dffits_ctgry_df$.Bonf.p)], id_var=glb_id_var, category_var=glb_category_var)
#
#car::influenceIndexPlot(glb_models_lst[["RFE.X.glm"]]$finalModel, id.n=3)

# myplot_parcoord(obs_df=glb_fitobs_df[, c(glb_id_var, glb_rsp_var,
#                                     "startprice.log10.predict.RFE.X.glmnet", 
#                            indep_vars[1:5])], obs_ix=hatobs_ix, id_var=glb_id_var)
# myplot_parcoord(x=glb_fitobs_df[, c(glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", 
#                            indep_vars[1:2])], obs_ix=hatobs_ix)
# hatvals <- hatvalues(glb_models_lst[["RFE.X.glm"]]$finalModel)
# hatobs_ix <- which(hatvals == max(hatvals))
# MASS::parcoord(x=glb_fitobs_df[, c(glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", 
#                            indep_vars[1:2])], var.label=TRUE)
#plot(hatvalues(glb_models_lst[["RFE.X.glm"]]$finalModel), type = "h")
#glb_fitobs_df[which(row.names(glb_fitobs_df) %in% c("972")), c(glb_id_var, glb_rsp_var, glb_rsp_var_raw, "sold", glb_category_var)]

#all.equal(glb_models_lst[[glb_sel_mdl_id]], glb_models_lst[[glb_fin_mdl_id]])

glbObsTrnOutliers <- list()
#car::outlierTest(glb_models_lst[["RFE.X.glm"]]$finalModel)
#glb_trnobs_df[which(row.names(glb_fitobs_df) %in% c("972")), c(glb_id_var, glb_rsp_var, glb_rsp_var_raw, "sold", glb_category_var)]

glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression) {
    glbMdlMethods <- c(NULL
        # deterministic
            #, "lm", 
            , "glm"
            #, "bayesglm"   # crashing w/ parallel processing
            , "glmnet", "rpart"
        # non-deterministic
            , "gbm", "rf" 
        # Unknown
            #, "nnet" , "avNNet" # predicts 1 for all obs in bid0_sp # runs 25 models per cv sample for tunelength=5
            , "svmLinear", "svmLinear2"
            #, "svmPoly"   # crashing w/ parallel processing #, "svmPoly" runs 75 models per cv sample for tunelength=5
            #, "svmRadial" # crashing w/ parallel processing
            , "earth"
            #, "bagEarth" # Takes a long time
        )
} else
# Classification - Add ada,bagEarth (auto feature selection)
    if (glb_is_binomial)
        glbMdlMethods <- c(NULL
        # deterministic                     
            , "glm"
            , "bayesglm" # crashing w/ parallel processing
            , "glmnet"
            , "nnet"
            , "rpart"
        # non-deterministic        
            , "gbm"
            , "avNNet" # runs 25 models per cv sample for tunelength=5      
            , "rf"
        # Unknown
            , "lda", "lda2"
                # svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
#             , "svmLinear", "svmLinear2", "svmPoly" #, "svmPoly" runs 75 models per cv sample for tunelength=5
#             ,   "svmRadial" 
            , "earth"
            , "bagEarth" # Takes a long time
        ) else
            glbMdlMethods <- c("rpart", "rf", "gbm")

glb_mdl_family_lst <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
#   methods: Choose from c(NULL, <method>, glbMdlMethods) 
glb_mdl_family_lst[["RFE.X"]] <- c("glmnet", "glm") # # setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid #non-NULL list is mandatory
glb_mdl_family_lst[["All.X"]] <- "glmnet" # non-NULL list is mandatory
glb_mdl_family_lst[["Best.Interact"]] <- "glmnet" # non-NULL list is mandatory

### bid1_sp
# glb_mdl_family_lst[["CSM.X"]] <- "glmnet"
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
#     # from RFE.X
#     , "startprice.dgt1.is9", "startprice.dcm2.is9", "startprice.dcm1.is9", "startprice.dgt2.is9"
#     #, "condition.fctr"
#     , "prdl.descr.my.fctr", "color.fctr"
#     #, "D.ratio.weight.sum.wrds.n"
#     , "cellular.fctr", "cellular.fctr:carrier.fctr"
#     
#     # from RFE.X.Interact
#     , "cellular.fctr:prdl.descr.my.fctr", "cellular.fctr:startprice.dgt2.is9", "cellular.fctr:startprice.dgt1.is9", "cellular.fctr:color.fctr"
#     , "cellular.fctr:condition.fctr" # RMSE up with keeping condition.fctr in the model
#                                 # RMSE & R.sq up with removing condition.fctr from the model
#     , "cellular.fctr:D.ratio.weight.sum.wrds.n"
#     )
### bid1_sp

### !_sp
# glb_mdl_family_lst[["CSM.X"]] <- "glmnet"
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
#         , "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio",
#         , "D.npnct15.log", "D.npnct03.log", "D.wrds.n.log", "D.chrs.n.log")
#         indep_vars <- union(setdiff(indep_vars, interact_vars_vctr),
#                                 paste(glb_category_var, interact_vars_vctr, 
#                             sep=ifelse(grepl("\\.fctr", glb_category_var), "*", ".fctr*")))
#         indep_vars <- union(setdiff(indep_vars, 
#                         c("startprice.log.diff", "startprice.unit9", "biddable", "cellular.fctr", "carrier.fctr")),
#                             c("startprice.log.diff*biddable", "startprice.unit9*biddable", "cellular.fctr*carrier.fctr"))
### !_sp

# Check if interaction features make fit better
# Check if tuning parameters make fit better
glb_tune_models_df <- data.frame()

    #RFE.X.avNNet    
### bid0_sp
    #   size=[1] 3 5 7 9; decay=[0] 1e-04 0.001  0.01   0.1; bag=[FALSE]; RMSE=1.3300906 
### bid1_sp
    #   size=[1] 3 5 7 9; decay=0 0.0001 [0.001] 0.01 0.1; bag=[FALSE]; RMSE=0.9285472
### bid0&1_sp

    #RFE.X.bagEarth
### bid0_sp
    #RFE.X.bagEarth degree=[1]; nprune=[33]; RMSE=0.1507259
### bid1_sp
    #RFE.X.bagEarth degree=[1]; nprune=[32]; RMSE=0.6379639
    #RFE.X.bagEarth degree=[1] 2 3; nprune=8 16 32 64 [128]; RMSE=0.6334405
    #RFE.X.bagEarth degree=1 [2]; nprune=16 32 64 128 [256]; RMSE=0.6211924

    #RFE.X.bagEarth degree=1 [2]; nprune=64 128 200 225 [256]; RMSE=0.6320776 (up)
    #RFE.X.bagEarth degree=[1] 2; nprune=64 128 225 256 [275]; RMSE=0.640644 (up)
    #RFE.X.bagEarth degree=1 [2] 3; nprune=64 128 200 [256] 300; RMSE=0.6496039 (up)
    #RFE.X.bagEarth degree=1 [2] 3; nprune=32 64 128 256 [512]; RMSE=0.6404529 (up)
    #RFE.X.bagEarth degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
#     ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
#     ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")    
# ))
### bid0&1_sp

### bid0_sp
    #RFE.X.earth degree=[1]; nprune=2  [9] 17 25 33; RMSE=0.1334478
### bid0_sp
    
    #RFE.X.gbm 
### bid0_sp    
    #   shrinkage=[0.1]; n.trees=50 100 150 [200] 250; RMSE=0.2062651
    #   shrinkage=0.00 0.05 0.10 0.15 [0.20]; n.trees=50 [100] 150 200 250; interaction.depth=1 [2] 3 4 5; n.minobsinnode=[10]; RMSE=0.2019453       
    #   shrinkage=0.00 0.05 [0.10] 0.15 0.20; n.trees=50 100 150 200 [250]; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313     
    #   shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313     
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
#     ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
#     ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
#     ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
#     ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
#     #seq(from=0.05,  to=0.25, by=0.05)
# ))
### bid1_sp
    #   shrinkage=[0.1]; n.trees=50 100 150 200 [250]; interaction.depth=1 2 3 4 [5]; n.minobsinnode=[10]; RMSE=0.5054172
#   shrinkage=0.03 [0.04] 0.05 0.06 0.07; n.trees=100 [150] 200 250 300; interaction.depth=2 3 4 5 [6]; n.minobsinnode=6  [8] 10 12 14; RMSE=0.5036430
#   shrinkage=0.03 [0.04] 0.05 0.06 0.07; n.trees=100 150 [200] 250 300; interaction.depth=3 4 5 [6] 7; n.minobsinnode=6 8 [10] 12 14; RMSE=0.502774

#   shrinkage=0.04; n.trees=200; interaction.depth=6; n.minobsinnode=10; RMSE=0.502774

#   shrinkage=[0.05] 0.10 0.15 0.20 0.25; n.trees=100 [150] 200 250 300; interaction.depth=2 3 [4] 5 6; n.minobsinnode=[10]; RMSE=0.5058678 (up)
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
#     ,data.frame(method = "gbm", parameter = "shrinkage", vals = "0.04")
#     ,data.frame(method = "gbm", parameter = "n.trees", vals = "200")
#     ,data.frame(method = "gbm", parameter = "interaction.depth", vals = "6")
#     ,data.frame(method = "gbm", parameter = "n.minobsinnode", vals = "10")
# ))
### bid0&1_sp

    #RFE.X.glmnet
### bid1_sp
    #   alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
### bid1_nosp
    #   alpha=0.325 0.550 0.775 0.8875 [1.000]; lambda=9.858855e-05 [0.0001971771] 0.0009152152 0.0042480525 0.0197177130; Accuracy=0.8455510
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
#     ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
#     ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")    
# ))

    #RFE.X.nnet    
### bid0_sp
    #   size=[1] 3 5 7 9; decay=[0] 1e-04 0.001  0.01   0.1; RMSE=1.3300906 
### bid1_sp
    #   size=1 3 5 7 [9]; decay=0e+00 1e-04 1e-03 1e-02 [1e-01]; RMSE=0.9289109
    #   size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
#     ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
#     ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")    
# ))
### bid0&1_sp

    #RFE.X.rf # Don't bother; results are not deterministic
### bid0_sp
    #       mtry=2  35  [68] 101 134; RMSE=0.1331992
    #       mtry=2  35  68 [101] 134; RMSE=0.1339974
### bid0_sp
### no_sp
    #       mtry=2  41  81 121 [161]; Accuracy=0.8398314
    #       mtry=41 81 [121] 161 181; Accuracy=0.8282403
### no_sp
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
#     ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))

    #RFE.X.rpart 
### bid0_sp    
    #   cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
### bid1_sp
    #   cp=0.001 [0.003] 0.005 0.007 0.009; RMSE=0.5186586
### bid0_nosp
    #   cp=0.004347826 [0.008695652] 0.017391304 0.021739130 0.034782609; Accuracy=0.8120340    
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()    
#     ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
    
    #RFE.X.svmLinear
### bid0_sp
    #   C=[1]; RMSE=0.1374094    
    #   C=1e-02 [0.1] 5e-01 1e+00 2e+00 3e+00 4e+00 1e+01 1e+02; RMSE=0.1271318
    #   C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
### bid1_sp
    #   C=[1]; RMSE=0.6614060
    #   C=1e-02 [1e-01] 1e+00 1e+01 1e+02; RMSE=0.6373977
    #   C=[0.05]  0.10  0.50  1.00 10.00; RMSE=0.6324697
    #   C=0.01 [0.05] 0.10 0.50 1.00; RMSE=0.6324697
    
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
#     ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
### bid0&1_sp

    #RFE.X.svmLinear2    
### bid0_sp
    #   cost=[0.25] 0.50 1.00 2.00 4.00; RMSE=0.1276354
    #   cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354 
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
#     ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
### bid1_sp
    #   cost=[0.25] 0.50 1.00 2.00 4.00; RMSE=0.6483622
    #   cost=[0.0625] 0.1250 0.25 0.50 1.00; RMSE=0.6335311
    #   cost=0.0312 [0.0625] 0.1250 0.25 0.50; RMSE=0.6335311
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
#     ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0312 0.0625 0.125 0.25 0.50")
# ))
### bid0&1_sp

    #RFE.X.svmPoly    
### bid0_sp
    #   degree=[1] 2 3; scale=0.001 0.01 [0.1] 1 10; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1276130
    #   degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
#     ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ))
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
#     ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
#     ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")    
# ))
### bid0_sp

    #RFE.X.svmRadial
### bid0_sp
    #   sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
### bid0_sp

    #data.frame(parameter="mtry",  min=080, max=100, by=10),
    
#glb_to_sav(); all.equal(sav_models_df, glb_models_df)
#glb_models_df <- subset(sav_models_df, id != "RFE.X.gbm"); print(sort(glb_models_df$id))
    
glb_preproc_methods <- NULL
    ### bid0_sp
#                         c("YeoJohnson", "center.scale", 
#                               # crashes with train: all the RMSE metric values are missing
#                                   #   probably due to interaction vars
#                                   "range",   "pca", "ica", 
#                                   "spatialSign")
    ### bid0_sp
    ### bid1_sp
#                     c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")
    ### bid1_sp

# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")

glbMdlMetric_terms <- NULL # or matrix(c(
#                               0,1,2,3,4,
#                               2,0,1,2,3,
#                               4,2,0,1,2,
#                               6,4,2,0,1,
#                               8,6,4,2,0
#                           ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression) 
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
#     confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
#     #print(confusion_mtrx)
#     #print(confusion_mtrx * glbMdlMetric_terms)
#     metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
#     names(metric) <- glbMdlMetricSummary
#     return(metric)
# }

glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL

glb_clf_proba_threshold <- NULL # 0.5

# Model selection criteria
if (glb_is_regression)
    glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
    #glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
    if (glb_is_binomial)
        glbMdlMetricsEval <- 
            c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else
        glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}

# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- NULL
    ### bid0_sp
#     c("RFE.X.glm"
#       #, "RFE.X.bayesglm"
#       , "RFE.X.glmnet", "RFE.X.rpart", "RFE.X.gbm", "RFE.X.rf", "RFE.X.svmLinear", "RFE.X.svmLinear2"
#       #, "RFE.X.svmPoly", "RFE.X.svmRadial"
#       , "RFE.X.earth", "RFE.X.bagEarth", "RFE.X.Interact.glmnet", "RFE.X.YeoJohnson.glmnet", "RFE.X.center.scale.glmnet", "RFE.X.spatialSign.glmnet")
    ### bid1_sp
    # "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
    # "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
    # c("RFE.X.spatialSign.rf", "RFE.X.YeoJohnson.rf", "RFE.X.center.scale.rf", "RFE.X.rf", "RFE.X.avNNet", "RFE.X.bagEarth", "RFE.X.earth", "RFE.X.gbm", "RFE.X.glmnet", "RFE.X.nnet", "RFE.X.svmLinear2", "RFE.X.glm", "RFE.X.svmLinear", "RFE.X.rpart")
    ### bid1_sp
    ### no_sp
#     c("RFE.X.rpart", "RFE.X.earth", "RFE.X.glmnet", "RFE.X.glm", "RFE.X.bayesglm", "RFE.X.nnet", "RFE.X.avNNet", "RFE.X.Interact.glmnet", "RFE.X.gbm"
#       #,"RFE.X.bagEarth" #takes a long time
#       )
    ### no_sp
    ### bid1_nosp      
#     c("CSM.X.glm", "CSM.X.bayesglm", "CSM.X.glmnet", "CSM.X.nnet", "CSM.X.avNNet", "CSM.X.rpart", "CSM.X.gbm", "CSM.X.rf", "CSM.X.lda", "CSM.X.lda2", "CSM.X.earth", "CSM.X.bagEarth", "CSM.X.Interact.glmnet") # Accuracy.OOB(Ensemble.repeatedcv.glmnet) = 0.8240223
    # Deleted models with repeated cor(CSM.X.gbm.prob)
#     c("CSM.X.glm", "CSM.X.bayesglm", "CSM.X.glmnet", "CSM.X.nnet", "CSM.X.avNNet", "CSM.X.rpart", "CSM.X.gbm", "CSM.X.rf", "CSM.X.lda2", "CSM.X.earth", "CSM.X.bagEarth") # Accuracy.OOB(Ensemble.repeatedcv.glmnet) = 0.8240223
    # Used step(ntv.glm) to select simplest model
#    c("CSM.X.glmnet", "CSM.X.nnet", "CSM.X.avNNet", "CSM.X.rpart", "CSM.X.gbm", "CSM.X.rf", "CSM.X.earth", "CSM.X.bagEarth") # Accuracy.OOB(Ensemble.repeatedcv.glmnet) = 0.8156425
    # Deleted models with cor(CSM.X.gbm.prob) between Q3 & Max
#    c("CSM.X.glm", "CSM.X.bayesglm", "CSM.X.glmnet", "CSM.X.nnet", "CSM.X.avNNet", "CSM.X.rpart", "CSM.X.gbm", "CSM.X.rf", "CSM.X.lda2") # Accuracy.OOB(Ensemble.repeatedcv.glmnet) = 0.8184358
    ### bid1_nosp

#paste(grep("CSM.X", names(glb_models_lst), fixed=TRUE, value=TRUE), collapse='",  "')

# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glb_fitobs_df[, grep(paste0("^", gsub(".", "\\.", glb_rsp_var_out, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glb_fitobs_df), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$importance))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indep_vars, glb_rsp_var), family = "binomial", data = glb_fitobs_df)
#step.glm <- step(ntv.glm)

glb_sel_mdl_id <- "All.X.glmnet" #NULL #select from c(NULL, "All.X.glmnet", "RFE.X.glmnet")
glb_fin_mdl_id <- NULL #select from c(NULL, glb_sel_mdl_id)

glb_dsp_cols <- c("sold", ".grpid", "color", "condition", "cellular", "carrier", "storage", "biddable", "startprice", "sprice.predict.diff")

glb_out_obs <- NULL # "all" for bid._sp # select from c(NULL, "all", "new", "trn")
glb_out_vars_lst <- list()
# glb_id_var will be the first output column, by default
### !_sp
glb_out_vars_lst[["Probability1"]] <- "%<d-% paste0(glb_rsp_var_out, glb_fin_mdl_id, '.prob')"
### !_sp   
### bid._sp
# glb_out_vars_lst[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glb_out_vars_lst[[paste0(head(unlist(strsplit(glb_rsp_var_out, "")), -1), collapse = "")]] <-
#     "%<d-% paste0(glb_rsp_var_out, glb_fin_mdl_id)"
### bid._sp   

glbOutStackFnames <- NULL #: default
    # c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
    # c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack
glb_out_pfx <- "ebayipads_txt2_n15_"
glb_save_envir <- FALSE # or TRUE

# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
                        trans_df = data.frame(id = 1:6,
    name = c("data.training.all","data.new",
           "model.selected","model.final",
           "data.training.all.prediction","data.new.prediction"),
    x=c(   -5,-5,-15,-25,-25,-35),
    y=c(   -5, 5,  0,  0, -5,  5)
                        ),
                        places_df=data.frame(id=1:4,
    name=c("bgn","fit.data.training.all","predict.data.new","end"),
    x=c(   -0,   -20,                    -30,               -40),
    y=c(    0,     0,                      0,                 0),
    M0=c(   3,     0,                      0,                 0)
                        ),
                        arcs_df=data.frame(
    begin=c("bgn","bgn","bgn",        
            "data.training.all","model.selected","fit.data.training.all",
            "fit.data.training.all","model.final",    
            "data.new","predict.data.new",
            "data.training.all.prediction","data.new.prediction"),
    end  =c("data.training.all","data.new","model.selected",
            "fit.data.training.all","fit.data.training.all","model.final",
            "data.training.all.prediction","predict.data.new",
            "predict.data.new","data.new.prediction",
            "end","end")
                        ))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())
## Loading required package: grid

glb_analytics_avl_objs <- NULL

glb_chunks_df <- myadd_chunk(NULL, "import.data")
##         label step_major step_minor label_minor    bgn end elapsed
## 1 import.data          1          0           0 23.954  NA      NA

Step 1.0: import data

chunk option: eval=

#glb_chunks_df <- myadd_chunk(NULL, "import.data")

glb_to_sav <- function() {
    sav_allobs_df <<- glb_allobs_df 
    sav_trnobs_df <<- glb_trnobs_df
    if (any(grepl("glb_fitobs_df", ls(envir=globalenv()), fixed=TRUE)) &&
        !is.null(glb_fitobs_df)) sav_fitobs_df <<- glb_fitobs_df    
    if (any(grepl("glb_OOBobs_df", ls(envir=globalenv()), fixed=TRUE)) &&
        !is.null(glb_OOBobs_df)) sav_OOBobs_df <<- glb_OOBobs_df    
    if (any(grepl("glb_newobs_df", ls(envir=globalenv()), fixed=TRUE)) &&
        !is.null(glb_newobs_df)) {
        #print("Attempting to save glb_newobs_df...")
        sav_newobs_df <<- glb_newobs_df    
    }

    if (any(grepl("glb_ctgry_df", ls(envir=globalenv()), fixed=TRUE)) &&
        !is.null(glb_ctgry_df)) sav_ctgry_df <<- glb_ctgry_df    

    if (!is.null(glb_models_lst )) sav_models_lst  <<- glb_models_lst
    if (!is.null(glb_models_df  )) sav_models_df   <<- glb_models_df

    if (any(grepl("glb_feats_df", ls(envir=globalenv()), fixed=TRUE)) &&
        !is.null(glb_feats_df)) sav_feats_df <<- glb_feats_df    
    if (any(grepl("glb_featsimp_df", ls(envir=globalenv()), fixed=TRUE)) &&
        !is.null(glb_featsimp_df)) sav_featsimp_df <<- glb_featsimp_df    
}

glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df", 
                                force_header=TRUE)
## [1] "Reading file ./data/eBayiPadTrain.csv..."
## [1] "dimensions of data in ./data/eBayiPadTrain.csv: 1,861 rows x 11 cols"
##                                                                                            description
## 1                                                        iPad is in 8.5+ out of 10 cosmetic condition!
## 2 Previously used, please read description. May show signs of use such as scratches to the screen and 
## 3                                                                                                     
## 4                                                                                                     
## 5 Please feel free to buy. All products have been thoroughly inspected, cleaned and tested to be 100% 
## 6                                                                                                     
##   biddable startprice               condition cellular carrier      color
## 1        0     159.99                    Used        0    None      Black
## 2        1       0.99                    Used        1 Verizon    Unknown
## 3        0     199.99                    Used        0    None      White
## 4        0     235.00 New other (see details)        0    None    Unknown
## 5        0     199.99      Seller refurbished  Unknown Unknown    Unknown
## 6        1     175.00                    Used        1    AT&T Space Gray
##   storage productline sold UniqueID
## 1      16      iPad 2    0    10001
## 2      16      iPad 2    1    10002
## 3      16      iPad 4    1    10003
## 4      16 iPad mini 2    0    10004
## 5 Unknown     Unknown    0    10005
## 6      32 iPad mini 2    1    10006
##                                                                                                        description
## 65                                                                                                                
## 283                                                              Pristine condition, comes with a case and stylus.
## 948  \211\333\317Used Apple Ipad 16 gig 1st generation in Great working condition and 100% functional.Very little 
## 1354                                                                                                              
## 1366         Item still in complete working order, minor scratches, normal wear and tear but no damage. screen is 
## 1840                                                                                                              
##      biddable startprice          condition cellular carrier      color
## 65          0     195.00               Used        0    None    Unknown
## 283         1      20.00               Used        0    None    Unknown
## 948         0     110.00 Seller refurbished        0    None      Black
## 1354        0     300.00               Used        0    None      White
## 1366        1     125.00               Used  Unknown Unknown    Unknown
## 1840        0     249.99               Used        1  Sprint Space Gray
##      storage productline sold UniqueID
## 65        16   iPad mini    0    10065
## 283       64      iPad 1    0    10283
## 948       32      iPad 1    0    10948
## 1354      16    iPad Air    1    11354
## 1366 Unknown      iPad 1    1    11366
## 1840      16    iPad Air    1    11840
##                                                                                            description
## 1856  Overall item is in good condition and is fully operational and ready to use. Comes with box and 
## 1857 Used. Tested. Guaranteed to work. Physical condition grade B+ does have some light scratches and 
## 1858     This item is brand new and was never used; however, the box and/or packaging has been opened.
## 1859                                                                                                  
## 1860     This unit has minor scratches on case and several small scratches on the display. \nIt is in 
## 1861  30 Day Warranty.  Fully functional engraved iPad 1st Generation with signs of normal wear which 
##      biddable startprice               condition cellular carrier
## 1856        0      89.50                    Used        1    AT&T
## 1857        0     239.95                    Used        0    None
## 1858        0     329.99 New other (see details)        0    None
## 1859        0     400.00                     New        0    None
## 1860        0      89.00      Seller refurbished        0    None
## 1861        0     119.99                    Used        1    AT&T
##           color storage productline sold UniqueID
## 1856    Unknown      16      iPad 1    0    11856
## 1857      Black      32      iPad 4    1    11857
## 1858 Space Gray      16    iPad Air    0    11858
## 1859       Gold      16 iPad mini 3    0    11859
## 1860      Black      64      iPad 1    1    11860
## 1861      Black      64      iPad 1    0    11861
## 'data.frame':    1861 obs. of  11 variables:
##  $ description: chr  "iPad is in 8.5+ out of 10 cosmetic condition!" "Previously used, please read description. May show signs of use such as scratches to the screen and " "" "" ...
##  $ biddable   : int  0 1 0 0 0 1 1 0 1 1 ...
##  $ startprice : num  159.99 0.99 199.99 235 199.99 ...
##  $ condition  : chr  "Used" "Used" "Used" "New other (see details)" ...
##  $ cellular   : chr  "0" "1" "0" "0" ...
##  $ carrier    : chr  "None" "Verizon" "None" "None" ...
##  $ color      : chr  "Black" "Unknown" "White" "Unknown" ...
##  $ storage    : chr  "16" "16" "16" "16" ...
##  $ productline: chr  "iPad 2" "iPad 2" "iPad 4" "iPad mini 2" ...
##  $ sold       : int  0 1 1 0 0 1 1 0 1 1 ...
##  $ UniqueID   : int  10001 10002 10003 10004 10005 10006 10007 10008 10009 10010 ...
##  - attr(*, "comment")= chr "glb_trnobs_df"
## NULL
# glb_trnobs_df <- read.delim("data/hygiene.txt", header=TRUE, fill=TRUE, sep="\t",
#                             fileEncoding='iso-8859-1')
# glb_trnobs_df <- read.table("data/hygiene.dat.labels", col.names=c("dirty"),
#                             na.strings="[none]")
# glb_trnobs_df$review <- readLines("data/hygiene.dat", n =-1)
# comment(glb_trnobs_df) <- "glb_trnobs_df"                                

# glb_trnobs_df <- data.frame()
# for (symbol in c("Boeing", "CocaCola", "GE", "IBM", "ProcterGamble")) {
#     sym_trnobs_df <- 
#         myimport_data(url=gsub("IBM", symbol, glb_trnng_url), comment="glb_trnobs_df", 
#                                     force_header=TRUE)
#     sym_trnobs_df$Symbol <- symbol
#     glb_trnobs_df <- myrbind_df(glb_trnobs_df, sym_trnobs_df)
# }
                                
# glb_trnobs_df <- 
#     glb_trnobs_df %>% dplyr::filter(Year >= 1999)
                                
if (glb_is_separate_newobs_dataset) {
    glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df", 
                                   force_header=TRUE)
    
    # To make plots / stats / checks easier in chunk:inspectORexplore.data
    glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df); 
    comment(glb_allobs_df) <- "glb_allobs_df"
} else {
    glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
    if (!glb_split_entity_newobs_datasets) {
        stop("Not implemented yet") 
        glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
                                          max(2, nrow(glb_trnobs_df) / 1000)),]                    
    } else      if (glb_split_newdata_method == "condition") {
            glb_newobs_df <- do.call("subset", 
                list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
            glb_trnobs_df <- do.call("subset", 
                list(glb_trnobs_df, parse(text=paste0("!(", 
                                                      glb_split_newdata_condition,
                                                      ")"))))
        } else if (glb_split_newdata_method == "sample") {
                require(caTools)
                
                set.seed(glb_split_sample.seed)
                split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw], 
                                      SplitRatio=(1-glb_split_newdata_size_ratio))
                glb_newobs_df <- glb_trnobs_df[!split, ] 
                glb_trnobs_df <- glb_trnobs_df[split ,]
        } else if (glb_split_newdata_method == "copy") {  
            glb_trnobs_df <- glb_allobs_df
            comment(glb_trnobs_df) <- "glb_trnobs_df"
            glb_newobs_df <- glb_allobs_df
            comment(glb_newobs_df) <- "glb_newobs_df"
        } else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")   

    comment(glb_newobs_df) <- "glb_newobs_df"
    myprint_df(glb_newobs_df)
    str(glb_newobs_df)

    if (glb_split_entity_newobs_datasets) {
        myprint_df(glb_trnobs_df)
        str(glb_trnobs_df)        
    }
}         
## [1] "Reading file ./data/eBayiPadTest.csv..."
## [1] "dimensions of data in ./data/eBayiPadTest.csv: 798 rows x 10 cols"
##                                                                                                  description
## 1                                                                                                   like new
## 2 Item is in great shape. I upgraded to the iPad Air 2 and don&#039;t need the mini any longer, even though 
## 3        This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer.
## 4                                                                                                           
## 5        Grade A condition means that the Ipad is 100% working condition. Cosmetically 8/9 out of 10 - Will 
## 6                   Brand new factory sealed iPad in an OPEN BOX...THE BOX ITSELF IS HEAVILY DISTRESSED(see 
##   biddable startprice                condition cellular carrier   color
## 1        0     105.00                     Used        1    AT&T Unknown
## 2        0     195.00                     Used        0    None Unknown
## 3        0     219.99                     Used        0    None Unknown
## 4        1     100.00                     Used        0    None Unknown
## 5        0     210.99 Manufacturer refurbished        0    None   Black
## 6        0     514.95  New other (see details)        0    None    Gold
##   storage productline UniqueID
## 1      32      iPad 1    11862
## 2      16 iPad mini 2    11863
## 3      64      iPad 3    11864
## 4      16   iPad mini    11865
## 5      32      iPad 3    11866
## 6      64  iPad Air 2    11867
##                                                                                               description
## 1                                                                                                like new
## 142                                             iPad mini 1st gen wi-fi 16gb is in perfect working order.
## 309     In excellent condition. Minor scratches on the back. Screen in mint condition. Comes in original 
## 312 iPad is in Great condition, the screen is in great condition showing only a few minor scratches, the 
## 320                                                                   Good condition and fully functional
## 369                                                                                                      
##     biddable startprice condition cellular carrier   color storage
## 1          0     105.00      Used        1    AT&T Unknown      32
## 142        1       0.99      Used        0    None Unknown      16
## 309        0     200.00      Used        1    AT&T   Black      32
## 312        1       0.99      Used        0    None Unknown      16
## 320        1      60.00      Used        0    None   White      16
## 369        1     197.97      Used        0    None Unknown      64
##     productline UniqueID
## 1        iPad 1    11862
## 142   iPad mini    12003
## 309      iPad 3    12170
## 312 iPad mini 2    12173
## 320      iPad 1    12181
## 369 iPad mini 3    12230
##                                                                                              description
## 793  Crack on digitizer near top. Top line of digitizer does not respond to touch. Other than that, all 
## 794                                                                                                     
## 795                                                                                                     
## 796                                                                                                     
## 797                                                                                                     
## 798 Slightly Used. Includes everything you need plus a nice leather case!\nThere is a slice mark on the 
##     biddable startprice                condition cellular carrier   color
## 793        0     104.00 For parts or not working        1 Unknown   Black
## 794        0      95.00                     Used        1    AT&T Unknown
## 795        1     199.99 Manufacturer refurbished        0    None   White
## 796        0     149.99                     Used        0    None Unknown
## 797        0       7.99                      New  Unknown Unknown Unknown
## 798        0     139.00                     Used        1 Unknown   Black
##     storage productline UniqueID
## 793      16      iPad 2    12654
## 794      64      iPad 1    12655
## 795      16      iPad 4    12656
## 796      16      iPad 2    12657
## 797 Unknown      iPad 3    12658
## 798      32     Unknown    12659
## 'data.frame':    798 obs. of  10 variables:
##  $ description: chr  "like new" "Item is in great shape. I upgraded to the iPad Air 2 and don&#039;t need the mini any longer, even though " "This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer." "" ...
##  $ biddable   : int  0 0 0 1 0 0 0 0 0 1 ...
##  $ startprice : num  105 195 220 100 211 ...
##  $ condition  : chr  "Used" "Used" "Used" "Used" ...
##  $ cellular   : chr  "1" "0" "0" "0" ...
##  $ carrier    : chr  "AT&T" "None" "None" "None" ...
##  $ color      : chr  "Unknown" "Unknown" "Unknown" "Unknown" ...
##  $ storage    : chr  "32" "16" "64" "16" ...
##  $ productline: chr  "iPad 1" "iPad mini 2" "iPad 3" "iPad mini" ...
##  $ UniqueID   : int  11862 11863 11864 11865 11866 11867 11868 11869 11870 11871 ...
##  - attr(*, "comment")= chr "glb_newobs_df"
## NULL
if ((num_nas <- sum(is.na(glb_trnobs_df[, glb_rsp_var_raw]))) > 0)
    stop("glb_trnobs_df$", glb_rsp_var_raw, " contains NAs for ", num_nas, " obs")

if (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
    warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
    warning("glb_newobs_df same as glb_allobs_df")

if (length(glb_drop_vars) > 0) {
    warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
    glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
    glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]    
    glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]    
}

#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Combine trnent & newobs into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test"; 
glbFeatsExclude <- union(glbFeatsExclude, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"

# Check for duplicates in glb_id_var
if (length(glb_id_var) == 0) {
    warning("using .rownames as identifiers for observations")
    glb_allobs_df$.rownames <- rownames(glb_allobs_df)
    glb_trnobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Train"))
    glb_newobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Test"))    
    glb_id_var <- ".rownames"
}
if (sum(duplicated(glb_allobs_df[, glb_id_var, FALSE])) > 0)
    stop(glb_id_var, " duplicated in glb_allobs_df")
glbFeatsExclude <- union(glbFeatsExclude, glb_id_var)

glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_trnobs_df <- glb_newobs_df <- NULL

# For Tableau
write.csv(glb_allobs_df, "data/eBayiPadAll.csv", row.names=FALSE)

#stop(here"); glb_to_sav()
# Make any data corrections here
glb_allobs_df[glb_allobs_df[, glb_id_var] == 10986, "cellular"] <- "1"
glb_allobs_df[glb_allobs_df[, glb_id_var] == 10986, "carrier"] <- "T-Mobile"

# - Merge glb_obs_stack_condition & glbObsDropCondition
# - Derive glb_obs_stack|drop_chk_vars from condition automatically
# - Implement glb_obs_stack_condition & glb_obs_stack_chk_vars options

dsp_partition_stats <- function(obs_df, vars=NULL) {
    
    lcl_vars <- NULL
    for (var in c(vars, glb_rsp_var_raw)) {
        if ((length(unique(obs_df[, var])) > 5) && is.numeric(obs_df[, var])) {
            cut_var <- paste0(var, ".cut.fctr")
            obs_df[, cut_var] <- cut(obs_df[, var], 3)
            lcl_vars <- union(lcl_vars, cut_var)
        } else lcl_vars <- union(lcl_vars, var)   
    }

    print("Partition stats:")
    print(mycreate_sqlxtab_df(obs_df, union(lcl_vars, ".src")))
    for (var in lcl_vars) {
        print(freq_df <- mycreate_sqlxtab_df(obs_df, union(var, ".src")))
        print(myplot_hbar(freq_df, ".src", ".n", colorcol_name=var))
    }
    print(mycreate_sqlxtab_df(obs_df, ".src"))
    
#     if (length(unique(glb_allobs_df[, glb_rsp_var_raw])) > 5) {
#         cut_var <- paste0(glb_rsp_var_raw, ".cut.fctr")
#         glb_allobs_df[, cut_var] <- cut(glb_allobs_df[, glb_rsp_var_raw], 3)
#         glbFeatsExclude <- union(glbFeatsExclude, cut_var)
#         glb_obs_stack_chk_vars <- union(cut_var, glb_obs_stack_chk_vars)
#     } else glb_obs_stack_chk_vars <- union(glb_rsp_var_raw, glb_obs_stack_chk_vars)
#     #glb_obs_stack_chk_vars <- union(glb_obs_stack_chk_vars, ".src")
#     print(mycreate_sqlxtab_df(glb_allobs_df, union(var, ".src")))
#     print(mycreate_sqlxtab_df(glb_allobs_df, union(glb_obs_stack_chk_vars, ".src")))
#     for (var in glb_obs_stack_chk_vars) {
#         print(mycreate_sqlxtab_df(glb_allobs_df, union(var, ".src")))
#     }
#     print(mycreate_sqlxtab_df(glb_allobs_df, ".src"))
    
}

myget_symbols <- function(txt) {
    if (is.null(txt)) return(NULL)
    #print(getParseData(parse(text=txt, keep.source=TRUE)))
    return(unique(subset(getParseData(parse(text=txt, keep.source=TRUE)), 
                         token == "SYMBOL")$text))
}
# tokens <- unlist(strsplit(gsub("[[:punct:]|[:space:]]", " ", glbObsDropCondition), " "))
# tokens <- tokens[tokens != ""]
# glb_obs_drop_chk_vars <- c("biddable") # or NULL

dsp_partition_stats(obs_df=glb_allobs_df, vars=myget_symbols(glbObsDropCondition))
## [1] "Partition stats:"
## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk
##      UniqueID.cut.fctr      productline sold  .src  .n
## 1  (1.18e+04,1.27e+04]           iPad 2   NA  Test 154
## 2  (1.18e+04,1.27e+04]        iPad mini   NA  Test 111
## 3     (1e+04,1.09e+04]           iPad 2    1 Train 104
## 4  (1.18e+04,1.27e+04]          Unknown   NA  Test  92
## 5  (1.09e+04,1.18e+04]        iPad mini    0 Train  91
## 6     (1e+04,1.09e+04]           iPad 1    1 Train  91
## 7  (1.18e+04,1.27e+04]           iPad 1   NA  Test  88
## 8  (1.09e+04,1.18e+04]          Unknown    0 Train  84
## 9  (1.09e+04,1.18e+04]           iPad 2    0 Train  82
## 10 (1.18e+04,1.27e+04]         iPad Air   NA  Test  74
## 11    (1e+04,1.09e+04]        iPad mini    1 Train  74
## 12 (1.18e+04,1.27e+04]           iPad 4   NA  Test  68
## 13 (1.09e+04,1.18e+04]       iPad Air 2    0 Train  65
## 14 (1.18e+04,1.27e+04]       iPad Air 2   NA  Test  62
## 15    (1e+04,1.09e+04]           iPad 3    1 Train  61
## 16 (1.09e+04,1.18e+04]         iPad Air    0 Train  58
## 17 (1.18e+04,1.27e+04]      iPad mini 2   NA  Test  56
## 18 (1.18e+04,1.27e+04]           iPad 3   NA  Test  55
## 19 (1.09e+04,1.18e+04]        iPad mini    1 Train  54
## 20    (1e+04,1.09e+04]           iPad 2    0 Train  52
## 21    (1e+04,1.09e+04]         iPad Air    1 Train  51
## 22 (1.09e+04,1.18e+04]           iPad 1    0 Train  50
## 23    (1e+04,1.09e+04]        iPad mini    0 Train  48
## 24 (1.09e+04,1.18e+04]          Unknown    1 Train  47
## 25 (1.09e+04,1.18e+04]           iPad 4    0 Train  45
## 26    (1e+04,1.09e+04]           iPad 1    0 Train  44
## 27    (1e+04,1.09e+04]           iPad 4    0 Train  43
## 28 (1.09e+04,1.18e+04]      iPad mini 3    0 Train  41
## 29 (1.09e+04,1.18e+04]           iPad 2    1 Train  40
## 30    (1e+04,1.09e+04]           iPad 4    1 Train  40
## 31    (1e+04,1.09e+04]         iPad Air    0 Train  39
## 32 (1.18e+04,1.27e+04]      iPad mini 3   NA  Test  38
## 33 (1.09e+04,1.18e+04]      iPad mini 2    0 Train  36
## 34    (1e+04,1.09e+04]       iPad Air 2    1 Train  36
## 35    (1e+04,1.09e+04]           iPad 3    0 Train  35
## 36 (1.09e+04,1.18e+04]           iPad 3    0 Train  34
## 37 (1.09e+04,1.18e+04]       iPad Air 2    1 Train  33
## 38    (1e+04,1.09e+04]          Unknown    0 Train  29
## 39    (1e+04,1.09e+04]          Unknown    1 Train  29
## 40 (1.09e+04,1.18e+04]           iPad 1    1 Train  28
## 41    (1e+04,1.09e+04]       iPad Air 2    0 Train  28
## 42    (1e+04,1.09e+04]      iPad mini 2    1 Train  28
## 43 (1.09e+04,1.18e+04]         iPad Air    1 Train  25
## 44 (1.09e+04,1.18e+04]      iPad mini 2    1 Train  20
## 45    (1e+04,1.09e+04]      iPad mini 2    0 Train  20
## 46 (1.09e+04,1.18e+04]           iPad 4    1 Train  19
## 47 (1.09e+04,1.18e+04]           iPad 3    1 Train  17
## 48    (1e+04,1.09e+04]      iPad mini 3    0 Train  17
## 49    (1e+04,1.09e+04]      iPad mini 3    1 Train  15
## 50 (1.09e+04,1.18e+04]      iPad mini 3    1 Train  12
## 51 (1.18e+04,1.27e+04]          Unknown    0 Train   9
## 52 (1.18e+04,1.27e+04]           iPad 1    0 Train   8
## 53 (1.18e+04,1.27e+04]       iPad Air 2    0 Train   7
## 54 (1.18e+04,1.27e+04]          Unknown    1 Train   6
## 55 (1.18e+04,1.27e+04]           iPad 1    1 Train   6
## 56 (1.18e+04,1.27e+04]        iPad mini    0 Train   6
## 57 (1.18e+04,1.27e+04]           iPad 2    0 Train   5
## 58 (1.18e+04,1.27e+04]           iPad 4    0 Train   5
## 59 (1.18e+04,1.27e+04]           iPad 4    1 Train   5
## 60 (1.18e+04,1.27e+04]         iPad Air    0 Train   5
## 61 (1.18e+04,1.27e+04]      iPad mini 3    0 Train   5
## 62 (1.18e+04,1.27e+04]           iPad 3    0 Train   4
## 63 (1.18e+04,1.27e+04]        iPad mini    1 Train   4
## 64 (1.09e+04,1.18e+04] iPad mini Retina    0 Train   3
## 65 (1.18e+04,1.27e+04]           iPad 2    1 Train   3
## 66 (1.18e+04,1.27e+04]           iPad 3    1 Train   2
## 67 (1.18e+04,1.27e+04]         iPad Air    1 Train   2
## 68 (1.18e+04,1.27e+04]       iPad Air 2    1 Train   2
## 69 (1.18e+04,1.27e+04]      iPad mini 2    0 Train   2
## 70    (1e+04,1.09e+04] iPad mini Retina    1 Train   2
## 71 (1.09e+04,1.18e+04]           iPad 5    1 Train   1
## 72 (1.09e+04,1.18e+04] iPad mini Retina    1 Train   1
## 73 (1.18e+04,1.27e+04]      iPad mini 2    1 Train   1
## 74 (1.18e+04,1.27e+04] iPad mini Retina    1 Train   1
## 75    (1e+04,1.09e+04] iPad mini Retina    0 Train   1
##     UniqueID.cut.fctr  .src  .n
## 1    (1e+04,1.09e+04] Train 887
## 2 (1.09e+04,1.18e+04] Train 886
## 3 (1.18e+04,1.27e+04]  Test 798
## 4 (1.18e+04,1.27e+04] Train  88

##         productline  .src  .n
## 1            iPad 2 Train 286
## 2         iPad mini Train 277
## 3            iPad 1 Train 227
## 4           Unknown Train 204
## 5          iPad Air Train 180
## 6        iPad Air 2 Train 171
## 7            iPad 4 Train 157
## 8            iPad 2  Test 154
## 9            iPad 3 Train 153
## 10        iPad mini  Test 111
## 11      iPad mini 2 Train 107
## 12          Unknown  Test  92
## 13      iPad mini 3 Train  90
## 14           iPad 1  Test  88
## 15         iPad Air  Test  74
## 16           iPad 4  Test  68
## 17       iPad Air 2  Test  62
## 18      iPad mini 2  Test  56
## 19           iPad 3  Test  55
## 20      iPad mini 3  Test  38
## 21 iPad mini Retina Train   8
## 22           iPad 5 Train   1

##   sold  .src   .n
## 1    0 Train 1001
## 2    1 Train  860
## 3   NA  Test  798

##    .src   .n
## 1 Train 1861
## 2  Test  798
if (!is.null(glbObsDropCondition)) {
    print(sprintf("Running glbObsDropCondition filter: %s", glbObsDropCondition))
    glb_allobs_df <- do.call("subset", 
                list(glb_allobs_df, parse(text=paste0("!(", glbObsDropCondition, ")"))))
    dsp_partition_stats(obs_df=glb_allobs_df, vars=myget_symbols(glbObsDropCondition))    
}
## [1] "Running glbObsDropCondition filter: (UniqueID %in% c(NULL\n                , 11234 #sold=0; 2 other dups(10306, 11503) are sold=1\n                , 11844 #sold=0; 3 other dups(11721, 11738, 11812) are sold=1\n                )) | \n            (productline %in% c('iPad 5', 'iPad mini Retina'))\n                    # | (biddable != 0) # bid0_sp\n                    # | (biddable == 0) # bid1_sp\n            "
## [1] "Partition stats:"
##      UniqueID.cut.fctr productline sold  .src  .n
## 1  (1.18e+04,1.27e+04]      iPad 2   NA  Test 154
## 2  (1.18e+04,1.27e+04]   iPad mini   NA  Test 111
## 3     (1e+04,1.09e+04]      iPad 2    1 Train 104
## 4  (1.18e+04,1.27e+04]     Unknown   NA  Test  92
## 5  (1.09e+04,1.18e+04]   iPad mini    0 Train  91
## 6     (1e+04,1.09e+04]      iPad 1    1 Train  91
## 7  (1.18e+04,1.27e+04]      iPad 1   NA  Test  88
## 8  (1.09e+04,1.18e+04]     Unknown    0 Train  84
## 9  (1.09e+04,1.18e+04]      iPad 2    0 Train  82
## 10 (1.18e+04,1.27e+04]    iPad Air   NA  Test  74
## 11    (1e+04,1.09e+04]   iPad mini    1 Train  74
## 12 (1.18e+04,1.27e+04]      iPad 4   NA  Test  68
## 13 (1.09e+04,1.18e+04]  iPad Air 2    0 Train  65
## 14 (1.18e+04,1.27e+04]  iPad Air 2   NA  Test  62
## 15    (1e+04,1.09e+04]      iPad 3    1 Train  61
## 16 (1.09e+04,1.18e+04]    iPad Air    0 Train  58
## 17 (1.18e+04,1.27e+04] iPad mini 2   NA  Test  56
## 18 (1.18e+04,1.27e+04]      iPad 3   NA  Test  55
## 19 (1.09e+04,1.18e+04]   iPad mini    1 Train  54
## 20    (1e+04,1.09e+04]      iPad 2    0 Train  52
## 21    (1e+04,1.09e+04]    iPad Air    1 Train  51
## 22 (1.09e+04,1.18e+04]      iPad 1    0 Train  49
## 23    (1e+04,1.09e+04]   iPad mini    0 Train  48
## 24 (1.09e+04,1.18e+04]     Unknown    1 Train  47
## 25 (1.09e+04,1.18e+04]      iPad 4    0 Train  45
## 26    (1e+04,1.09e+04]      iPad 1    0 Train  44
## 27    (1e+04,1.09e+04]      iPad 4    0 Train  43
## 28 (1.09e+04,1.18e+04] iPad mini 3    0 Train  41
## 29 (1.09e+04,1.18e+04]      iPad 2    1 Train  40
## 30    (1e+04,1.09e+04]      iPad 4    1 Train  40
## 31    (1e+04,1.09e+04]    iPad Air    0 Train  39
## 32 (1.18e+04,1.27e+04] iPad mini 3   NA  Test  38
## 33 (1.09e+04,1.18e+04] iPad mini 2    0 Train  36
## 34    (1e+04,1.09e+04]  iPad Air 2    1 Train  36
## 35    (1e+04,1.09e+04]      iPad 3    0 Train  35
## 36 (1.09e+04,1.18e+04]      iPad 3    0 Train  34
## 37 (1.09e+04,1.18e+04]  iPad Air 2    1 Train  33
## 38    (1e+04,1.09e+04]     Unknown    0 Train  29
## 39    (1e+04,1.09e+04]     Unknown    1 Train  29
## 40 (1.09e+04,1.18e+04]      iPad 1    1 Train  28
## 41    (1e+04,1.09e+04]  iPad Air 2    0 Train  28
## 42    (1e+04,1.09e+04] iPad mini 2    1 Train  28
## 43 (1.09e+04,1.18e+04]    iPad Air    1 Train  25
## 44 (1.09e+04,1.18e+04] iPad mini 2    1 Train  20
## 45    (1e+04,1.09e+04] iPad mini 2    0 Train  20
## 46 (1.09e+04,1.18e+04]      iPad 4    1 Train  19
## 47 (1.09e+04,1.18e+04]      iPad 3    1 Train  17
## 48    (1e+04,1.09e+04] iPad mini 3    0 Train  17
## 49    (1e+04,1.09e+04] iPad mini 3    1 Train  15
## 50 (1.09e+04,1.18e+04] iPad mini 3    1 Train  12
## 51 (1.18e+04,1.27e+04]     Unknown    0 Train   9
## 52 (1.18e+04,1.27e+04]      iPad 1    0 Train   7
## 53 (1.18e+04,1.27e+04]  iPad Air 2    0 Train   7
## 54 (1.18e+04,1.27e+04]     Unknown    1 Train   6
## 55 (1.18e+04,1.27e+04]      iPad 1    1 Train   6
## 56 (1.18e+04,1.27e+04]   iPad mini    0 Train   6
## 57 (1.18e+04,1.27e+04]      iPad 2    0 Train   5
## 58 (1.18e+04,1.27e+04]      iPad 4    0 Train   5
## 59 (1.18e+04,1.27e+04]      iPad 4    1 Train   5
## 60 (1.18e+04,1.27e+04]    iPad Air    0 Train   5
## 61 (1.18e+04,1.27e+04] iPad mini 3    0 Train   5
## 62 (1.18e+04,1.27e+04]      iPad 3    0 Train   4
## 63 (1.18e+04,1.27e+04]   iPad mini    1 Train   4
## 64 (1.18e+04,1.27e+04]      iPad 2    1 Train   3
## 65 (1.18e+04,1.27e+04]      iPad 3    1 Train   2
## 66 (1.18e+04,1.27e+04]    iPad Air    1 Train   2
## 67 (1.18e+04,1.27e+04]  iPad Air 2    1 Train   2
## 68 (1.18e+04,1.27e+04] iPad mini 2    0 Train   2
## 69 (1.18e+04,1.27e+04] iPad mini 2    1 Train   1
##     UniqueID.cut.fctr  .src  .n
## 1    (1e+04,1.09e+04] Train 884
## 2 (1.09e+04,1.18e+04] Train 880
## 3 (1.18e+04,1.27e+04]  Test 798
## 4 (1.18e+04,1.27e+04] Train  86

##    productline  .src  .n
## 1       iPad 2 Train 286
## 2    iPad mini Train 277
## 3       iPad 1 Train 225
## 4      Unknown Train 204
## 5     iPad Air Train 180
## 6   iPad Air 2 Train 171
## 7       iPad 4 Train 157
## 8       iPad 2  Test 154
## 9       iPad 3 Train 153
## 10   iPad mini  Test 111
## 11 iPad mini 2 Train 107
## 12     Unknown  Test  92
## 13 iPad mini 3 Train  90
## 14      iPad 1  Test  88
## 15    iPad Air  Test  74
## 16      iPad 4  Test  68
## 17  iPad Air 2  Test  62
## 18 iPad mini 2  Test  56
## 19      iPad 3  Test  55
## 20 iPad mini 3  Test  38

##   sold  .src  .n
## 1    0 Train 995
## 2    1 Train 855
## 3   NA  Test 798

##    .src   .n
## 1 Train 1850
## 2  Test  798
# Check for duplicates by all features
require(gdata)
## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
## 
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
## 
## Attaching package: 'gdata'
## 
## The following object is masked from 'package:stats':
## 
##     nobs
## 
## The following object is masked from 'package:utils':
## 
##     object.size
#print(names(glb_allobs_df))
dup_allobs_df <- glb_allobs_df[duplicated2(subset(glb_allobs_df, 
                                                  select=-c(UniqueID, sold, .src))), ]
dup_allobs_df <- orderBy(~productline+description+startprice+biddable, dup_allobs_df)
print(sprintf("Found %d duplicates by all features:", nrow(dup_allobs_df)))
## [1] "Found 304 duplicates by all features:"
myprint_df(dup_allobs_df)
##      description biddable startprice                condition cellular
## 1711                    1       0.99 For parts or not working  Unknown
## 2608                    1       0.99 For parts or not working  Unknown
## 293                     1       5.00                     Used  Unknown
## 478                     1       5.00                     Used  Unknown
## 385                     0      15.00                     Used        0
## 390                     0      15.00                     Used        0
##      carrier   color storage productline sold UniqueID  .src
## 1711 Unknown Unknown      16     Unknown    1    11711 Train
## 2608 Unknown Unknown      16     Unknown   NA    12608  Test
## 293  Unknown   White      16     Unknown    1    10293 Train
## 478  Unknown   White      16     Unknown    1    10478 Train
## 385     None   Black      16     Unknown    0    10385 Train
## 390     None   Black      16     Unknown    0    10390 Train
##      description biddable startprice                condition cellular
## 1956                    1       0.99                     Used        0
## 828                     1     249.97 Manufacturer refurbished        1
## 3                       0     199.99                     Used        0
## 1649                    0     209.00 For parts or not working  Unknown
## 2111                    1     200.00                     Used        0
## 172                     0     269.00                     Used        0
##      carrier      color storage productline sold UniqueID  .src
## 1956    None    Unknown      16      iPad 2   NA    11956  Test
## 828  Unknown      Black      64      iPad 2    0    10828 Train
## 3       None      White      16      iPad 4    1    10003 Train
## 1649 Unknown    Unknown      16    iPad Air    0    11649 Train
## 2111    None Space Gray      64 iPad mini 2   NA    12111  Test
## 172     None    Unknown      32 iPad mini 2    0    10172 Train
##      description biddable startprice condition cellular carrier color
## 8                       0     329.99       New        0    None White
## 660                     0     329.99       New        0    None White
## 319                     0     345.00       New        0    None  Gold
## 1886                    0     345.00       New        0    None  Gold
## 1363                    0     498.88       New        1 Verizon  Gold
## 1394                    0     498.88       New        1 Verizon  Gold
##      storage productline sold UniqueID  .src
## 8         16 iPad mini 3    0    10008 Train
## 660       16 iPad mini 3    0    10660 Train
## 319       16 iPad mini 3    1    10319 Train
## 1886      16 iPad mini 3   NA    11886  Test
## 1363      16 iPad mini 3    0    11363 Train
## 1394      16 iPad mini 3    0    11394 Train
# print(dup_allobs_df[, c(glb_id_var, glb_rsp_var_raw, 
#                          "description", "startprice", "biddable")])
# write.csv(dup_allobs_df[, c("UniqueID"), FALSE], "ebayipads_dups.csv", row.names=FALSE)

dupobs_df <- tidyr::unite(dup_allobs_df, "allfeats", -c(sold, UniqueID, .src), sep="#")
# dupobs_df <- dplyr::group_by(dupobs_df, allfeats)
# dupobs_df <- dupobs_df[, "UniqueID", FALSE]
# dupobs_df <- ungroup(dupobs_df)
# 
# dupobs_df$.rownames <- row.names(dupobs_df)
grpobs_df <- data.frame(allfeats=unique(dupobs_df[, "allfeats"]))
grpobs_df$.grpid <- row.names(grpobs_df)
dupobs_df <- merge(dupobs_df, grpobs_df)

# dupobs_tbl <- table(dupobs_df$.grpid)
# print(max(dupobs_tbl))
# print(dupobs_tbl[which.max(dupobs_tbl)])
# print(dupobs_df[dupobs_df$.grpid == names(dupobs_tbl[which.max(dupobs_tbl)]), ])
# print(dupobs_df[dupobs_df$.grpid == 106, ])
# for (grpid in c(9, 17, 31, 36, 53))
#     print(dupobs_df[dupobs_df$.grpid == grpid, ])
dupgrps_df <- as.data.frame(table(dupobs_df$.grpid, dupobs_df$sold, useNA="ifany"))
names(dupgrps_df)[c(1,2)] <- c(".grpid", "sold")
dupgrps_df$.grpid <- as.numeric(as.character(dupgrps_df$.grpid))
dupgrps_df <- tidyr::spread(dupgrps_df, sold, Freq)
names(dupgrps_df)[-1] <- paste("sold", names(dupgrps_df)[-1], sep=".")
dupgrps_df$.freq <- sapply(1:nrow(dupgrps_df), function(row) sum(dupgrps_df[row, -1]))
myprint_df(orderBy(~-.freq, dupgrps_df))
##     .grpid sold.0 sold.1 sold.NA .freq
## 40      40      0      6       3     9
## 106    106      0      4       1     5
## 9        9      0      1       3     4
## 17      17      0      3       1     4
## 36      36      0      3       1     4
## 53      53      0      2       2     4
##     .grpid sold.0 sold.1 sold.NA .freq
## 10      10      0      2       0     2
## 42      42      0      1       1     2
## 57      57      1      0       1     2
## 66      66      1      0       1     2
## 91      91      0      1       1     2
## 101    101      0      1       1     2
##     .grpid sold.0 sold.1 sold.NA .freq
## 130    130      1      0       1     2
## 131    131      1      1       0     2
## 132    132      0      1       1     2
## 133    133      2      0       0     2
## 134    134      0      1       1     2
## 135    135      2      0       0     2
print("sold Conflicts:")
## [1] "sold Conflicts:"
print(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0)))
##     .grpid sold.0 sold.1 sold.NA .freq
## 4        4      1      1       0     2
## 22      22      1      1       0     2
## 23      23      1      1       0     2
## 74      74      1      1       0     2
## 83      83      1      1       0     2
## 84      84      1      1       0     2
## 95      95      1      1       0     2
## 102    102      1      1       0     2
## 109    109      1      1       0     2
## 111    111      1      1       0     2
## 122    122      1      1       0     2
## 131    131      1      1       0     2
#dupobs_df[dupobs_df$.grpid == 4, ]
glb_allobs_df <- merge(glb_allobs_df, dupobs_df[, c(glb_id_var, ".grpid")], 
                       by=glb_id_var, all.x=TRUE)
if (nrow(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0) & (sold.0 != sold.1))) > 0)
    stop("Duplicate conflicts are resolvable")
#subset(glb_allobs_df, .grpid %in% c(25))
#mydsp_obs(list(productline.contains="iPad 1", storage.contains="16", color.contains="Black", carrier.contains="None", cellular.contains="0", condition.contains="Used", startprice=80), cols=c("productline", "storage", "color", "carrier", "cellular", "condition", "startprice", "sold"))

print("Test & Train Groups:")
## [1] "Test & Train Groups:"
print(subset(dupgrps_df, (sold.NA > 0)))
##     .grpid sold.0 sold.1 sold.NA .freq
## 1        1      0      1       1     2
## 5        5      1      0       1     2
## 7        7      0      0       2     2
## 8        8      1      0       1     2
## 9        9      0      1       3     4
## 12      12      0      0       2     2
## 14      14      0      1       1     2
## 15      15      0      0       2     2
## 17      17      0      3       1     4
## 18      18      0      2       1     3
## 19      19      0      2       1     3
## 24      24      0      2       1     3
## 26      26      1      0       1     2
## 28      28      1      0       1     2
## 30      30      0      1       1     2
## 32      32      0      0       2     2
## 33      33      0      1       1     2
## 35      35      0      2       1     3
## 36      36      0      3       1     4
## 37      37      0      0       2     2
## 38      38      0      1       1     2
## 40      40      0      6       3     9
## 41      41      0      0       2     2
## 42      42      0      1       1     2
## 43      43      0      1       1     2
## 44      44      0      2       1     3
## 47      47      0      1       1     2
## 48      48      0      0       2     2
## 49      49      0      1       2     3
## 51      51      0      1       1     2
## 53      53      0      2       2     4
## 54      54      0      1       1     2
## 55      55      1      0       2     3
## 56      56      1      0       1     2
## 57      57      1      0       1     2
## 58      58      0      0       2     2
## 59      59      1      0       1     2
## 60      60      1      0       1     2
## 63      63      0      1       1     2
## 66      66      1      0       1     2
## 67      67      1      0       1     2
## 68      68      0      0       2     2
## 69      69      1      0       1     2
## 73      73      0      1       1     2
## 76      76      0      2       1     3
## 86      86      0      0       2     2
## 87      87      1      0       1     2
## 89      89      1      0       1     2
## 90      90      0      0       2     2
## 91      91      0      1       1     2
## 93      93      0      1       1     2
## 94      94      1      0       1     2
## 99      99      0      1       1     2
## 101    101      0      1       1     2
## 103    103      0      1       1     2
## 104    104      1      0       1     2
## 106    106      0      4       1     5
## 107    107      0      1       1     2
## 108    108      0      1       1     2
## 112    112      1      0       1     2
## 114    114      0      1       1     2
## 115    115      0      1       1     2
## 116    116      1      0       1     2
## 117    117      0      2       1     3
## 118    118      0      1       1     2
## 121    121      1      0       1     2
## 124    124      1      0       1     2
## 128    128      0      1       1     2
## 130    130      1      0       1     2
## 132    132      0      1       1     2
## 134    134      0      1       1     2
glbFeatsExclude <- c(".grpid", glbFeatsExclude)

#stop(here"); glb_to_sav(); all.equal(sav_allobs_df, glb_allobs_df); glb_allobs_df <- sav_allobs_df

if (!is.null(glbInpMerge)) {
    print("Running glbInpMerge specs...")
    obsMrg <- data.frame()
    for (fName in glbInpMerge$fnames) {
        print(sprintf("    Appending rows from %s...", fName))
        obsMrg <- rbind(obsMrg, read.csv(fName))
    }
    glb_allobs_df <- merge(glb_allobs_df, obsMrg, all.x = TRUE)
}
## [1] "Running glbInpMerge specs..."
## [1] "    Appending rows from ebayipads_finmdl_bid0_sp_out.csv..."
## [1] "    Appending rows from ebayipads_mdlens_bid1_sp_out.csv..."
dsp_partition_stats(obs_df = glb_allobs_df,
                    vars = myget_symbols(glb_obs_repartition_train_condition))
## [1] "Partition stats:"
##   sold  .src  .n
## 1    0 Train 995
## 2    1 Train 855
## 3   NA  Test 798
##   sold  .src  .n
## 1    0 Train 995
## 2    1 Train 855
## 3   NA  Test 798

##    .src   .n
## 1 Train 1850
## 2  Test  798
if (!is.null(glb_obs_repartition_train_condition)) {
    print(sprintf("Running glb_obs_repartition_train_condition filter: %s",
                  glb_obs_repartition_train_condition))
#     glb_allobs_df <- mutate(glb_allobs_df, .src=ifelse(!is.na(sold) & (sold == 1),
#                             "Train", "Test"))
#     glb_allobs_df <- mutate_(glb_allobs_df, 
#                         .src=interp(ifelse(eval(parse(text="!is.na(sold) & (sold == 1)")),
#                                         "Train", "Test")))
#     glb_allobs_df <- within(glb_allobs_df, {
#         .src <- ifelse(eval(parse(text="!is.na(sold) & (sold == 1)")),
#                                         "Train", "Test")
#     })
#     glb_allobs_df <- within(glb_allobs_df, {
#         if(eval(parse(text="!is.na(sold) & (sold == 1)"))) .src <- "Train" else
#             .src <- "Test"
#     })
#     with(glb_allobs_df, {
#         src <- ifelse(eval(parse(text="!is.na(sold) & (sold == 1)")),
#                                         "Train", "Test")
#     })
#     glb_allobs_df$.src <- sapply(1:nrow(glb_allobs_df), function (row_ix) ifelse)
#     glb_allobs_df[parse(text=paste0("!(", glbObsDropCondition, ")")), ".src"] <- do.call("subset", 
#                 list(glb_allobs_df, ))
    
    glb_trnobs_df <- do.call("subset", list(glb_allobs_df, 
                        parse(text=paste0(" (", glb_obs_repartition_train_condition, ")"))))
    glb_trnobs_df$.src <- "Train"
    glb_newobs_df <- do.call("subset", list(glb_allobs_df, 
                        parse(text=paste0("!(", glb_obs_repartition_train_condition, ")"))))
    glb_newobs_df$.src <- "Test"
    glb_allobs_df <- rbind(glb_trnobs_df, glb_newobs_df)

    dsp_partition_stats(obs_df = glb_allobs_df,
                        vars = myget_symbols(glb_obs_repartition_train_condition))    
}

glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)
##          label step_major step_minor label_minor    bgn    end elapsed
## 1  import.data          1          0           0 23.954 40.783  16.829
## 2 inspect.data          2          0           0 40.783     NA      NA

Step 2.0: inspect data

#print(str(glb_allobs_df))
#View(glb_allobs_df)

dsp_class_dstrb <- function(var) {
    xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
    rownames(xtab_df) <- xtab_df$.src
    xtab_df <- subset(xtab_df, select=-.src)
    print(xtab_df)
    print(xtab_df / rowSums(xtab_df, na.rm=TRUE))    
}    

# Performed repeatedly in other chunks
glb_chk_data <- function(featsExclude = glbFeatsExclude, 
                         fctrMaxUniqVals = glbFctrMaxUniqVals) {
    # Histogram of predictor in glb_trnobs_df & glb_newobs_df
    print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
    
    if (glb_is_classification) 
        dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df), 
                                   glb_rsp_var, glb_rsp_var_raw))
    mycheck_problem_data(glb_allobs_df, featsExclude, fctrMaxUniqVals)
}
glb_chk_data()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 798 rows containing non-finite values (stat_bin).
## Loading required package: reshape2

##       sold.0 sold.1 sold.NA
## Test      NA     NA     798
## Train    995    855      NA
##          sold.0    sold.1 sold.NA
## Test         NA        NA       1
## Train 0.5378378 0.4621622      NA
## [1] "numeric data missing in : "
## sold 
##  798 
## [1] "numeric data w/ 0s in : "
## biddable     sold 
##     1437      995 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description   condition    cellular     carrier       color     storage 
##        1513           0           0           0           0           0 
## productline      .grpid 
##           0          NA
# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
    glb_allobs_df[, glb_rsp_var] <- 
        glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
    mycheck_map_results(mapd_df=glb_allobs_df, 
                        from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
        
    if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}
##   sold sold.fctr  .n
## 1    0         N 995
## 2    1         Y 855
## 3   NA      <NA> 798
## Warning: Removed 1 rows containing missing values (position_stack).

##       sold.fctr.N sold.fctr.Y sold.fctr.NA
## Test           NA          NA          798
## Train         995         855           NA
##       sold.fctr.N sold.fctr.Y sold.fctr.NA
## Test           NA          NA            1
## Train   0.5378378   0.4621622           NA
# check distribution of all numeric data
dsp_numeric_feats_dstrb <- function(feats_vctr) {
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(ceiling(length(feats_vctr) / 2.0), 2)))
    pltIx <- 1
    for (feat in feats_vctr) {
        #print(sprintf("feat: %s", feat))
        if (glb_is_regression)
            gp <- myplot_scatter(df=glb_allobs_df, ycol_name=glb_rsp_var, xcol_name=feat,
                                 smooth=TRUE)
        if (glb_is_classification)
            #gp <- myplot_box(df=glb_allobs_df, ycol_names=feat, xcol_name=glb_rsp_var)
            gp <- myplot_violin(glb_allobs_df, ycol_names = feat, xcol_name = glb_rsp_var)
        if (inherits(glb_allobs_df[, feat], "factor"))
            gp <- gp + facet_wrap(reformulate(feat))
        print(gp + labs(title = feat), 
              vp = viewport(layout.pos.row = ceiling(pltIx / 2.0), 
                            layout.pos.col = ((pltIx - 1) %% 2) + 1))  
        
        pltIx <- pltIx + 1        
    }
}
# dsp_numeric_feats_dstrb(setdiff(names(glb_allobs_df), union(myfind_chr_cols_df(glb_allobs_df), c(glb_rsp_var_raw, glb_rsp_var)))[2:6])              # dsp_numeric_feats_dstrb(c("startprice", "sprice.root3", "sprice.predict.diff"))                                      
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
    require(plyr)
    
    set.seed(169)
    obs_df <- mutate(obs_df,
#         <col_name>.NA=is.na(<col_name>),

#         <col_name>.fctr=factor(<col_name>, 
#                     as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))), 
#         <col_name>.fctr=relevel(factor(<col_name>, 
#                     as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
#                                   "<ref_val>"), 
#         <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")), 
#                               as.factor(c("R", "<ref_val>")),
#                               ref="<ref_val>"),

          # This doesn't work - use sapply instead
#         <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)), 
#         
#         Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
#         Year=year(Date.my),
#         Month=months(Date.my),
#         Weekday=weekdays(Date.my)

#         <col_name>=<table>[as.character(<col2_name>)],
#         <col_name>=as.numeric(<col2_name>),

#         <col_name> = trunc(<col2_name> / 100),

        .rnorm = rnorm(n=nrow(obs_df))
                        )

    # If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails  
    # Transformations not handled by mutate
#     obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df), 
#         function(row_ix) grep(obs_df[row_ix, "<col_name>"],
#                               levels(obs_df[row_ix, "<col_name>.fctr"])))
    
    #print(summary(obs_df))
    #print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
    return(obs_df)
}
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)
## Loading required package: plyr
require(dplyr)
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## 
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## 
## The following objects are masked from 'package:gdata':
## 
##     combine, first, last
## 
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Merge some <descriptor>
# glb_allobs_df$<descriptor>.my <- glb_allobs_df$<descriptor>
# glb_allobs_df[grepl("\\bAIRPORT\\b", glb_allobs_df$<descriptor>.my),
#               "<descriptor>.my"] <- "AIRPORT"

# Check distributions of newly transformed / extracted vars
#   Enhancement: remove vars that were displayed ealier
dsp_numeric_feats_dstrb(feats_vctr=setdiff(names(glb_allobs_df), 
        c(myfind_chr_cols_df(glb_allobs_df), glb_rsp_var_raw, glb_rsp_var, 
          glbFeatsExclude)))

#   Convert factors to dummy variables
#   Build splines   require(splines); bsBasis <- bs(training$age, df=3)

#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches

# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) & 
#                         <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))

# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])

# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <- 
#   mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <- 
#   mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df, 
#             <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>))) 
# print(mycreate_sqlxtab_df(glb_allobs_df, c("<col1_name>", "<col2_name>")))

# print(<col2_name>_min_entity_arr <- 
#    sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <- 
#    sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))

# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("CocaCola", "ProcterGamble")), 
#                   "Date.POSIX", "StockPrice", facet_row_colnames="Symbol") + 
#     geom_vline(xintercept=as.numeric(as.POSIXlt("2003-03-01"))) +
#     geom_vline(xintercept=as.numeric(as.POSIXlt("1983-01-01")))        
#         )
# print(myplot_line(subset(glb_trnobs_df, Date.POSIX > as.POSIXct("2004-01-01")), 
#                   "Date.POSIX", "StockPrice") +
#     geom_line(aes(color=Symbol)) + 
#     coord_cartesian(xlim=c(as.POSIXct("1990-01-01"),
#                            as.POSIXct("2000-01-01"))) +     
#     coord_cartesian(ylim=c(0, 250)) +     
#     geom_vline(xintercept=as.numeric(as.POSIXlt("1997-09-01"))) +
#     geom_vline(xintercept=as.numeric(as.POSIXlt("1997-11-01")))        
#         )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") + 
#         geom_point(data=subset(glb_allobs_df, <condition>), 
#                     mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5) +
#         geom_vline(xintercept=84))

glb_chunks_df <- myadd_chunk(glb_chunks_df, "scrub.data", major.inc=FALSE)
##          label step_major step_minor label_minor    bgn    end elapsed
## 2 inspect.data          2          0           0 40.783 45.356   4.574
## 3   scrub.data          2          1           1 45.357     NA      NA

Step 2.1: scrub data

mycheck_problem_data(glb_allobs_df, featsExclude = glbFeatsExclude, 
                     fctrMaxUniqVals = glbFctrMaxUniqVals)
## [1] "numeric data missing in : "
##      sold sold.fctr 
##       798       798 
## [1] "numeric data w/ 0s in : "
## biddable     sold 
##     1437      995 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description   condition    cellular     carrier       color     storage 
##        1513           0           0           0           0           0 
## productline      .grpid 
##           0          NA
findOffendingCharacter <- function(x, maxStringLength=256){  
  print(x)
  for (c in 1:maxStringLength){
    offendingChar <- substr(x,c,c)
    #print(offendingChar) #uncomment if you want the indiv characters printed
    #the next character is the offending multibyte Character
  }    
}
# string_vector <- c("test", "Se\x96ora", "works fine")
# lapply(string_vector, findOffendingCharacter)
# lapply(glb_allobs_df$description[29], findOffendingCharacter)

dsp_hdlxtab <- function(str) 
    print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
                           c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")

dsp_catxtab <- function(str) 
    print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
        c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")

# Merge some categories
# glb_allobs_df$myCategory <-
#     plyr::revalue(glb_allobs_df$myCategory, c(      
#         "#Business Day#Dealbook"            = "Business#Business Day#Dealbook",
#         "#Business Day#Small Business"      = "Business#Business Day#Small Business",
#         "dummy" = "dummy"
#     ))

# ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
#                           mycreate_sqlxtab_df(glb_allobs_df,
#     c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"), 
#             row.names=FALSE)

# ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df, 
#                        myCategory + NewsDesk + SectionName + SubsectionName ~ 
#                            Popular.fctr, sum, value.var=".n"))
# myprint_df(ctgry_cast_df)
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"), 
#             row.names=FALSE)

# print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var], 
#                              useNA="ifany"))

dsp_chisq.test <- function(...) {
    sel_df <- glb_allobs_df[sel_obs(...) & 
                            !is.na(glb_allobs_df$Popular), ]
    sel_df$.marker <- 1
    ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
    mrg_df <- merge(ref_df[, c(glb_id_var, "Popular")],
                    sel_df[, c(glb_id_var, ".marker")], all.x=TRUE)
    mrg_df[is.na(mrg_df)] <- 0
    print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
    print("Rows:Selected; Cols:Popular")
    #print(mrg_tbl)
    print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")

# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ], 
#                           c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))

# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))

# glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)

print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))
##          
##           AT&T None Other Sprint T-Mobile Unknown Verizon
##   0          0 1586     0      0        0       0       0
##   1        288    0     3     36       28     172     196
##   Unknown    4    4     2      0        0     329       0
# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) & 
#               (glb_allobs_df$carrier %in% c("AT&T", "Other")), 
#               c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) & 
              (glb_allobs_df$carrier %in% c("AT&T", "Other")), 
              "cellular"] <- "1"
# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) & 
#               (glb_allobs_df$carrier %in% c("None")), 
#               c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) & 
              (glb_allobs_df$carrier %in% c("None")), 
              "cellular"] <- "0"
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))
##          
##           AT&T None Other Sprint T-Mobile Unknown Verizon
##   0          0 1590     0      0        0       0       0
##   1        292    0     5     36       28     172     196
##   Unknown    0    0     0      0        0     329       0

Step 2.1: scrub data

glb_chunks_df <- myadd_chunk(glb_chunks_df, "transform.data", major.inc=FALSE)
##            label step_major step_minor label_minor    bgn    end elapsed
## 3     scrub.data          2          1           1 45.357 46.438   1.081
## 4 transform.data          2          2           2 46.439     NA      NA
### Mapping dictionary
#sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_map_vars)) {
    for (feat in glb_map_vars) {
        map_df <- myimport_data(url=glb_map_urls[[feat]], 
                                            comment="map_df", 
                                           print_diagn=TRUE)
        glb_allobs_df <- mymap_codes(glb_allobs_df, feat, names(map_df)[2], 
                                     map_df, map_join_col_name=names(map_df)[1], 
                                     map_tgt_col_name=names(map_df)[2])
    }
    glbFeatsExclude <- union(glbFeatsExclude, glb_map_vars)
}

### Forced Assignments
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (feat in glb_assign_vars) {
    new_feat <- paste0(feat, ".my")
    print(sprintf("Forced Assignments for: %s -> %s...", feat, new_feat))
    glb_allobs_df[, new_feat] <- glb_allobs_df[, feat]
    
    pairs <- glb_assign_pairs_lst[[feat]]
    for (pair_ix in 1:length(pairs$from)) {
        if (is.na(pairs$from[pair_ix]))
            nobs <- nrow(filter(glb_allobs_df, 
                                is.na(eval(parse(text=feat),
                                            envir=glb_allobs_df)))) else
            nobs <- sum(glb_allobs_df[, feat] == pairs$from[pair_ix])
        #nobs <- nrow(filter(glb_allobs_df, is.na(Married.fctr)))    ; print(nobs)
        
        if ((is.na(pairs$from[pair_ix])) && (is.na(pairs$to[pair_ix])))
            stop("what are you trying to do ???")
        if (is.na(pairs$from[pair_ix]))
            glb_allobs_df[is.na(glb_allobs_df[, feat]), new_feat] <- 
                pairs$to[pair_ix] else
            glb_allobs_df[glb_allobs_df[, feat] == pairs$from[pair_ix], new_feat] <- 
                pairs$to[pair_ix]
                    
        print(sprintf("    %s -> %s for %s obs", 
                      pairs$from[pair_ix], pairs$to[pair_ix], format(nobs, big.mark=",")))
    }

    glbFeatsExclude <- union(glbFeatsExclude, glb_assign_vars)
}

### Derivations using mapping functions
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (new_feat in glb_derive_vars) {
    print(sprintf("Creating new feature: %s...", new_feat))
    args_lst <- NULL 
    for (arg in glbFeatsDerive[[new_feat]]$args) 
        args_lst[[arg]] <- glb_allobs_df[, arg]
    glb_allobs_df[, new_feat] <- do.call(glbFeatsDerive[[new_feat]]$mapfn, args_lst)
}
## [1] "Creating new feature: sprice.root2..."
## [1] "Creating new feature: sprice.log10..."
## [1] "Creating new feature: sprice.d20nexp..."
## [1] "Creating new feature: sprice.predict.diff..."
## [1] "Creating new feature: spdiff.cut.fctr..."
## [1] "Creating new feature: descr.my..."
## [1] "Creating new feature: prdl.my.fctr..."
#stop(here")
#hex_vctr <- c("\n", "\211", "\235", "\317", "\333")
hex_regex <- paste0(c("\n", "\211", "\235", "\317", "\333"), collapse="|")
for (obs_id in c(10029, 10948, 10136, 10178, 11514, 11904, 12157, 12210, 12659)) {
#     tmp_str <- unlist(strsplit(glb_allobs_df[row_pos, "descr.my"], ""))
#     glb_allobs_df[row_pos, "descr.my"] <- paste0(tmp_str[!tmp_str %in% hex_vctr],
#                                                          collapse="")
    row_pos <- which(glb_allobs_df$UniqueID == obs_id)
#     glb_allobs_df[row_pos, "descr.my"] <- 
#         gsub(hex_regex, " ", glb_allobs_df[row_pos, "descr.my"])
}

Step 2.2: transform data

#```{r extract_features, cache=FALSE, eval=!is.null(glbFeatsText)}
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)
##              label step_major step_minor label_minor    bgn    end elapsed
## 4   transform.data          2          2           2 46.439 47.049    0.61
## 5 extract.features          3          0           0 47.050     NA      NA
extract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")
##                  label step_major step_minor label_minor    bgn end
## 1 extract.features_bgn          1          0           0 47.057  NA
##   elapsed
## 1      NA
# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# 
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1, 
#                                                    "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df), 
#                                                    "<col_name>"]
                                                   
# glb_allobs_df <- mutate(glb_allobs_df,
#     A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
#                     )
# 
# glb_trnobs_df <- mutate(glb_trnobs_df,
#                     )
# 
# glb_newobs_df <- mutate(glb_newobs_df,
#                     )

#   Convert dates to numbers 
#       typically, dates come in as chars; 
#           so this must be done before converting chars to factors

#stop(here"); sav_allobs_df <- glb_allobs_df #; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_date_vars)) {
    glb_allobs_df <- cbind(glb_allobs_df, 
        myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars, 
                           id_vars=glb_id_var, rsp_var=glb_rsp_var))
    for (sfx in c("", ".POSIX"))
        glbFeatsExclude <- 
            union(glbFeatsExclude, 
                    paste(glb_date_vars, sfx, sep=""))

    for (feat in glb_date_vars) {
        glb_allobs_df <- orderBy(reformulate(paste0(feat, ".POSIX")), glb_allobs_df)
#         print(myplot_scatter(glb_allobs_df, xcol_name=paste0(feat, ".POSIX"),
#                              ycol_name=glb_rsp_var, colorcol_name=glb_rsp_var))
        print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >=
                                               strptime("2012-12-01", "%Y-%m-%d"), ], 
                             xcol_name=paste0(feat, ".POSIX"),
                             ycol_name=glb_rsp_var, colorcol_name=paste0(feat, ".wkend")))

        # Create features that measure the gap between previous timestamp in the data
        require(zoo)
        z <- zoo(as.numeric(as.POSIXlt(glb_allobs_df[, paste0(feat, ".POSIX")])))
        glb_allobs_df[, paste0(feat, ".zoo")] <- z
        print(head(glb_allobs_df[, c(glb_id_var, feat, paste0(feat, ".zoo"))]))
        print(myplot_scatter(glb_allobs_df[glb_allobs_df[,  paste0(feat, ".POSIX")] >
                                            strptime("2012-10-01", "%Y-%m-%d"), ], 
                            xcol_name=paste0(feat, ".zoo"), ycol_name=glb_rsp_var,
                            colorcol_name=glb_rsp_var))
        b <- zoo(, seq(nrow(glb_allobs_df)))
        
        last1 <- as.numeric(merge(z-lag(z, -1), b, all=TRUE)); last1[is.na(last1)] <- 0
        glb_allobs_df[, paste0(feat, ".last1.log")] <- log(1 + last1)
        print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[, 
                                                    paste0(feat, ".last1.log")] > 0, ], 
                               ycol_names=paste0(feat, ".last1.log"), 
                               xcol_name=glb_rsp_var))
        
        last2 <- as.numeric(merge(z-lag(z, -2), b, all=TRUE)); last2[is.na(last2)] <- 0
        glb_allobs_df[, paste0(feat, ".last2.log")] <- log(1 + last2)
        print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[, 
                                                    paste0(feat, ".last2.log")] > 0, ], 
                               ycol_names=paste0(feat, ".last2.log"), 
                               xcol_name=glb_rsp_var))
        
        last10 <- as.numeric(merge(z-lag(z, -10), b, all=TRUE)); last10[is.na(last10)] <- 0
        glb_allobs_df[, paste0(feat, ".last10.log")] <- log(1 + last10)
        print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[, 
                                                    paste0(feat, ".last10.log")] > 0, ], 
                               ycol_names=paste0(feat, ".last10.log"), 
                               xcol_name=glb_rsp_var))
        
        last100 <- as.numeric(merge(z-lag(z, -100), b, all=TRUE)); last100[is.na(last100)] <- 0
        glb_allobs_df[, paste0(feat, ".last100.log")] <- log(1 + last100)
        print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[, 
                                                    paste0(feat, ".last100.log")] > 0, ], 
                               ycol_names=paste0(feat, ".last100.log"), 
                               xcol_name=glb_rsp_var))
        
        glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
        glbFeatsExclude <- union(glbFeatsExclude, 
                                                c(paste0(feat, ".zoo")))
        # all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
        # all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
        # all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
        # all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
        # all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
        # 
        # 
        # # order table
        # all2 = all2[order(all2$id),]
        # 
        # ## fill in NAs
        # # count averages
        # na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
        #     last1=mean(last1, na.rm=TRUE),
        #     last3=mean(last3, na.rm=TRUE),
        #     last5=mean(last5, na.rm=TRUE),
        #     last10=mean(last10, na.rm=TRUE),
        #     last20=mean(last20, na.rm=TRUE),
        #     last50=mean(last50, na.rm=TRUE)
        # )
        # 
        # # fill in averages
        # na.merge = merge(all2, na.avg, by=c("weekend","hour"))
        # na.merge = na.merge[order(na.merge$id),]
        # for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
        #     y = paste0(i, ".y")
        #     idx = is.na(all2[[i]])
        #     all2[idx,][[i]] <- na.merge[idx,][[y]]
        # }
        # rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
    }
}
rm(last1, last10, last100)
## Warning in rm(last1, last10, last100): object 'last1' not found
## Warning in rm(last1, last10, last100): object 'last10' not found
## Warning in rm(last1, last10, last100): object 'last100' not found
#   Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, 
            paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)
##                                 label step_major step_minor label_minor
## 1                extract.features_bgn          1          0           0
## 2 extract.features_factorize.str.vars          2          0           0
##      bgn    end elapsed
## 1 47.057 47.074   0.017
## 2 47.074     NA      NA
#stop(here"); sav_allobs_df <- glb_allobs_df; #glb_allobs_df <- sav_allobs_df
print(str_vars <- myfind_chr_cols_df(glb_allobs_df))
##   description     condition      cellular       carrier         color 
## "description"   "condition"    "cellular"     "carrier"       "color" 
##       storage   productline          .src        .grpid      descr.my 
##     "storage" "productline"        ".src"      ".grpid"    "descr.my"
if (length(str_vars <- setdiff(str_vars, 
                               c(glbFeatsExclude, glbFeatsText))) > 0) {
    for (var in str_vars) {
        warning("Creating factors of string variable: ", var, 
                ": # of unique values: ", length(unique(glb_allobs_df[, var])))
        glb_allobs_df[, paste0(var, ".fctr")] <- 
            relevel(factor(glb_allobs_df[, var]),
                    names(which.max(table(glb_allobs_df[, var], useNA = "ifany"))))
    }
    glbFeatsExclude <- union(glbFeatsExclude, str_vars)
}
## Warning: Creating factors of string variable: condition: # of unique
## values: 6
## Warning: Creating factors of string variable: cellular: # of unique values:
## 3
## Warning: Creating factors of string variable: carrier: # of unique values:
## 7
## Warning: Creating factors of string variable: color: # of unique values: 5
## Warning: Creating factors of string variable: storage: # of unique values:
## 5
if (!is.null(glbFeatsText)) {
    require(foreach)
    require(gsubfn)
    require(stringr)
    require(tm)
    
    extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, 
            paste0("extract.features_", "process.text"), major.inc=TRUE)
    
    chk_pattern_freq <- function(rex_str, ignore.case=TRUE) {
        match_mtrx <- str_extract_all(txt_vctr, regex(rex_str, ignore_case=ignore.case), 
                                      simplify=TRUE)
        match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
        names(match_df) <- "pattern"
        return(mycreate_sqlxtab_df(match_df, "pattern"))        
    }

#     match_lst <- gregexpr("\\bok(?!ay)", txt_vctr[746], ignore.case = FALSE, perl=TRUE); print(match_lst)
    dsp_pattern <- function(rex_str, ignore.case=TRUE, print.all=TRUE) {
        match_lst <- gregexpr(rex_str, txt_vctr, ignore.case = ignore.case, perl=TRUE)
        match_lst <- regmatches(txt_vctr, match_lst)
        match_df <- data.frame(matches=sapply(match_lst, 
                                              function (elems) paste(elems, collapse="#")))
        match_df <- subset(match_df, matches != "")
        if (print.all)
            print(match_df)
        return(match_df)
    }
    
    dsp_matches <- function(rex_str, ix) {
        print(match_pos <- gregexpr(rex_str, txt_vctr[ix], perl=TRUE))
        print(str_sub(txt_vctr[ix], (match_pos[[1]] / 100) *  99 +   0, 
                                    (match_pos[[1]] / 100) * 100 + 100))        
    }

    myapply_gsub <- function(...) {
        if ((length_lst <- length(names(gsub_map_lst))) == 0)
            return(txt_vctr)
        for (ptn_ix in 1:length_lst) {
            if ((ptn_ix %% 10) == 0)
                print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix, 
                                length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
            txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]], 
                               txt_vctr, ...)
        }
        return(txt_vctr)
    }    

    myapply_txtmap <- function(txt_vctr, ...) {
        nrows <- nrow(glb_txt_map_df)
        for (ptn_ix in 1:nrows) {
            if ((ptn_ix %% 10) == 0)
                print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix, 
                                nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
            txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"], 
                             glb_txt_map_df[ptn_ix, "rpl_str"], 
                               txt_vctr, ...)
        }
        return(txt_vctr)
        #print(txt_vctr <- glb_allobs_df[glb_allobs_df$UniqueID == 11329, "descr.my"])
        #strsplit(txt_vctr, "")[[1]][1]
        #ptn_ix <- 2; glb_txt_map_df[ptn_ix, ]
        #gsub(glb_txt_map_df[ptn_ix, "rex_str"], glb_txt_map_df[ptn_ix, "rpl_str"], txt_vctr)
        #print(match_lst <- gregexpr(glb_txt_map_df[ptn_ix, "rex_str"], txt_vctr))
        #strsplit(glb_txt_map_df[ptn_ix, "rex_str"], "")[[1]]
    }    

    chk.equal <- function(bgn, end) {
        print(all.equal(sav_txt_lst[["Headline"]][bgn:end], 
                        glb_txt_chr_lst[["Headline"]][bgn:end]))
    }    
    dsp.equal <- function(bgn, end) {
        print(sav_txt_lst[["Headline"]][bgn:end])
        print(glb_txt_chr_lst[["Headline"]][bgn:end])
    }    
#sav_txt_lst <- glb_txt_chr_lst; all.equal(sav_txt_lst, glb_txt_chr_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_chr_lst[["Headline"]][1:4200])
#chk.equal( 1, 100)
#dsp.equal(86, 90)
    
    txt_map_filename <- paste0(glb_txt_munge_filenames_pfx, "map.csv")
    if (!file.exists(txt_map_filename))
        stop(txt_map_filename, " not found!")
    glb_txt_map_df <- read.csv(txt_map_filename, comment.char="#", strip.white=TRUE)
    glb_txt_chr_lst <- list(); 
    print(sprintf("Building glb_txt_chr_lst..."))
    glb_txt_chr_lst <- foreach(txt_var = glbFeatsText) %dopar% {   
#     for (txt_var in glbFeatsText) {
        txt_vctr <- glb_allobs_df[, txt_var]
        names(txt_vctr) <- glb_allobs_df[, glb_id_var]
        
        # myapply_txtmap shd be created as a tm_map::content_transformer ?
        #print(glb_txt_map_df)
        #txt_var=glbFeatsText[3]; txt_vctr <- glb_txt_chr_lst[[txt_var]]
        #print(rex_str <- glb_txt_map_df[3, "rex_str"])
        #print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
        #print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])        
        #print(rex_str <- glb_txt_map_df[glb_txt_map_df$rpl_str == "versus", "rex_str"])             
        #print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
        #ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
        #gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
        #grep("Hong Hong", txt_vctr, value=TRUE)
    
        txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)    
    }
    names(glb_txt_chr_lst) <- glbFeatsText

    for (txt_var in glbFeatsText) {
        print(sprintf("Remaining OK in %s:", txt_var))
        txt_vctr <- glb_txt_chr_lst[[txt_var]]
        
        print(chk_pattern_freq(rex_str <- "(?<!(BO|HO|LO))OK(?!(E\\!|ED|IE|IN|S ))",
                               ignore.case=FALSE))
        match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
        for (row in row.names(match_df))
            dsp_matches(rex_str, ix=as.numeric(row))

        print(chk_pattern_freq(rex_str <- "Ok(?!(a\\.|ay|in|ra|um))", ignore.case=FALSE))
        match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
        for (row in row.names(match_df))
            dsp_matches(rex_str, ix=as.numeric(row))

        print(chk_pattern_freq(rex_str <- "(?<!( b| B| c| C| g| G| j| M| p| P| w| W| r| Z|\\(b|ar|bo|Bo|co|Co|Ew|gk|go|ho|ig|jo|kb|ke|Ke|ki|lo|Lo|mo|mt|no|No|po|ra|ro|sm|Sm|Sp|to|To))ok(?!(ay|bo|e |e\\)|e,|e\\.|eb|ed|el|en|er|es|ey|i |ie|in|it|ka|ke|ki|ly|on|oy|ra|st|u |uc|uy|yl|yo))",
                               ignore.case=FALSE))
        match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
        for (row in row.names(match_df))
            dsp_matches(rex_str, ix=as.numeric(row))
    }    
    # txt_vctr <- glb_txt_chr_lst[[glbFeatsText[1]]]
    # print(chk_pattern_freq(rex_str <- "(?<!( b| c| C| p|\\(b|bo|co|lo|Lo|Sp|to|To))ok(?!(ay|e |e\\)|e,|e\\.|ed|el|en|es|ey|ie|in|on|ra))", ignore.case=FALSE))
    # print(chk_pattern_freq(rex_str <- "ok(?!(ay|el|on|ra))", ignore.case=FALSE))
    # dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
    # dsp_matches(rex_str, ix=8)
    # substr(txt_vctr[86], 5613, 5620)
    # substr(glb_allobs_df[301, "review"], 550, 650)

#stop(here"); sav_txt_lst <- glb_txt_chr_lst    
    for (txt_var in glbFeatsText) {
        print(sprintf("Remaining Acronyms in %s:", txt_var))
        txt_vctr <- glb_txt_chr_lst[[txt_var]]
        
        print(chk_pattern_freq(rex_str <- "([[:upper:]]\\.( *)){2,}", ignore.case=FALSE))
        
        # Check for names
        print(subset(chk_pattern_freq(rex_str <- "(([[:upper:]]+)\\.( *)){1}",
                                      ignore.case=FALSE),
                     .n > 1))
        # dsp_pattern(rex_str="(OK\\.( *)){1}", ignore.case=FALSE)
        # dsp_matches(rex_str="(OK\\.( *)){1}", ix=557)
        #dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)(\\B)", ix=461)
        #dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)", ix=461)        
        #print(str_sub(txt_vctr[676], 10100, 10200))
        #print(str_sub(txt_vctr[74], 1, -1))        
    }

    for (txt_var in glbFeatsText) {
        re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
        print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
        txt_vctr <- glb_txt_chr_lst[[txt_var]]        
        print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE), 
                                             grepl("( |-)[[:upper:]]", pattern))))
        print("    consider cleaning if relevant to problem domain; geography name; .n > 1")
        #grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
        #grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
    }        
        
#stop(here"); sav_txt_lst <- glb_txt_chr_lst    
    for (txt_var in glbFeatsText) {
        re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
        print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))        
        txt_vctr <- glb_txt_chr_lst[[txt_var]]                
        print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE), 
                                             grepl(".", pattern))))
        #grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)        
    }    

    for (txt_var in glbFeatsText) {
        re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
        print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))        
        txt_vctr <- glb_txt_chr_lst[[txt_var]]
        if (nrow(filtered_df <- subset(chk_pattern_freq(re_str, ignore.case=FALSE), 
                                             grepl(".", pattern))) > 0)
            print(orderBy(~ -.n +pattern, filtered_df))
        #grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
        #grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
        #grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)        
        #grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
        #grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
    }    

    find_cmpnd_wrds <- function(txt_vctr) {
        # Enhancements:
        #   - arg should be txt_corpus instead of txt_vctr
        
        txt_corpus <- Corpus(VectorSource(txt_vctr))
        txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy = TRUE)
        txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy = TRUE)
        txt_corpus <- tm_map(txt_corpus, removePunctuation,
                             preserve_intra_word_dashes = TRUE, lazy = FALSE)
        
        # Defaulting to Tf since TfIdf with normalize = TRUE throws a warning for empty docs
        terms_mtrx <- as.matrix(TermDocumentMatrix(txt_corpus,
                                                   control = list(weighting = weightTf)))
        terms_df <- orderBy(~ -Tf, data.frame(term = dimnames(terms_mtrx)$Terms,
                                              Tf = rowSums(terms_mtrx)))
        
        cmpnd_df <- subset(terms_df, grepl("-", term))
        if (nrow(cmpnd_df) == 0) {
            print("   No compounded terms found")
            return(FALSE)
        }
        
        txt_compound_filename <- paste0(glb_txt_munge_filenames_pfx, "compound.csv")
        if (!file.exists(txt_compound_filename))
            stop(txt_compound_filename, " not found!")
        filter_df <- read.csv(txt_compound_filename, comment.char="#", strip.white=TRUE)
        cmpnd_df$filter <- FALSE
        for (row_ix in 1:nrow(filter_df))
            cmpnd_df[!cmpnd_df$filter, "filter"] <- 
            grepl(filter_df[row_ix, "rex_str"], 
                  cmpnd_df[!cmpnd_df$filter, "term"], ignore.case=TRUE)
        cmpnd_df <- subset(cmpnd_df, !filter)
        # Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
        #   "net-a-porter" gets converted to "net-aporter"
        #grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
        #grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
        #txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
        #grep("\\b(across|longer)-(\\w)", cmpnd_df$term, ignore.case=TRUE, value=TRUE)
        #grep("(\\w)-(affected|term)\\b", cmpnd_df$term, ignore.case=TRUE, value=TRUE)
        
        print(sprintf("nrow(cmpnd_df): %d", nrow(cmpnd_df)))
        myprint_df(cmpnd_df)
    }

    # This should be run after glb_txt_corpus_lst is created with tolower
    extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, 
            paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
    
#     for (txt_var in glbFeatsText) {
#         print(sprintf("Remaining compound terms in %s: ", txt_var))        
#         find_cmpnd_wrds(txt_vctr = glb_txt_chr_lst[[txt_var]])
#         #grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
#         #rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
#     }

    extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, 
            paste0("extract.features_", "build.corpus"), major.inc=TRUE)
    
    get_txt_terms <- function(terms_TDM) {
        terms_mtrx <- as.matrix(as.TermDocumentMatrix(terms_TDM))
        docms_mtrx <- as.matrix(as.DocumentTermMatrix(terms_TDM))        
        terms_df <- data.frame(term = dimnames(terms_mtrx)$Terms,
                               weight = rowSums(terms_mtrx),
                               freq = rowSums(terms_mtrx > 0))
        terms_df$pos <- 1:nrow(terms_df)
        terms_df$cor.y <- 
            cor(docms_mtrx[glb_allobs_df$.src == "Train",], 
                as.numeric(glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var]),
                              use = "pairwise.complete.obs")
        terms_df$cor.y.abs <- abs(terms_df$cor.y)
#         .rnorm.cor.y.abs <- abs(cor(glb_allobs_df[glb_allobs_df$.src == "Train", ".rnorm"],
#                         as.numeric(glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var]),
#                                 use = "pairwise.complete.obs"))
        
        terms_df$chisq.stat <- NA; terms_df$chisq.pval <- NA
        for (ix in 1:nrow(terms_df)) {
        #for (ix in 1:743) {
            if (length(unique(docms_mtrx[glb_allobs_df$.src == "Train", ix])) > 1) {
                chisq <- chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix], 
                                    glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var])
                terms_df[ix, "chisq.stat"] <- chisq$statistic
                terms_df[ix, "chisq.pval"] <- chisq$p.value 
            }
        }
        
        nzv_df <- nzv(docms_mtrx[glb_allobs_df$.src == "Train",], freqCut = glb_nzv_freqCut,
                   uniqueCut = glb_nzv_uniqueCut, saveMetrics = TRUE)
        terms_df$nzv.freqRatio <- nzv_df$freqRatio
#         terms_df$nzv.freqRatio.cut.fctr <- cut(terms_df$nzv.freqRatio, 
#                                                 breaks = sort(c(min(terms_df$nzv.freqRatio), 
#                                                                 glb_nzv_freqCut,
#                                                                 max(terms_df$nzv.freqRatio))))
        terms_df$nzv.percentUnique <- nzv_df$percentUnique
#         terms_df$nzv.percentUnique.cut.fctr <- cut(terms_df$nzv.percentUnique, 
#                     breaks = sort(c(min(terms_df$nzv.percentUnique) - .Machine$double.neg.eps, 
#                                                             glb_nzv_uniqueCut,
#                                                             max(terms_df$nzv.percentUnique))))
#         terms_df$nzv.quad.fctr <- as.factor(paste0("fRatio:", terms_df$nzv.freqRatio.cut.fctr,
#                                             "\n%Unq:", terms_df$nzv.percentUnique.cut.fctr))
        terms_df$nzv <- nzv_df$nzv
        
        for (cls in unique(glb_allobs_df[, glb_txt_cor_var])) {
            if (!is.na(cls))
                terms_df[, paste0("weight.", as.character(cls))] <- 
                    colSums(t(terms_mtrx) * 
                            as.numeric(!is.na(glb_allobs_df[, glb_txt_cor_var]) &
                                        (glb_allobs_df[, glb_txt_cor_var] == cls))) else
                terms_df[, paste0("weight.", as.character(cls))] <- 
                    colSums(t(terms_mtrx) * 
                            as.numeric(is.na(glb_allobs_df[, glb_txt_cor_var])))
        }    
        
        # Check all calls to get_terms_DTM_terms to change returned order assumption
        return(terms_df <- orderBy(~ -weight, terms_df))
    }
    #plt_full_df <- get_terms_DTM_terms(terms_DTM=glb_full_terms_DTM_lst[[txt_var]])
    
    get_corpus_terms <- function(txt_corpus) {
        return(terms_df <- get_txt_terms(terms_TDM = 
                        TermDocumentMatrix(txt_corpus, control = glb_txt_terms_control)))
    }
    
    myreplacePunctuation <- function(x) {
        return(gsub("[[:punct:]]+", " ", gsub("('|-)", "", x)))
    }
    
#stop(here"); glb_to_sav()    
    glb_txt_corpus_lst <- list()
    print(sprintf("Building glb_txt_corpus_lst..."))
    glb_txt_corpus_lst <- foreach(txt_var = glbFeatsText, .verbose = FALSE) %dopar% {
    #glb_txt_corpus_lst <- foreach(txt_var = glbFeatsText, .verbose = TRUE) %do% {        
    #for (txt_var in glbFeatsText) {
        txt_corpus <- Corpus(VectorSource(glb_txt_chr_lst[[txt_var]]))
        txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy = TRUE)
        txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy = TRUE) #nuppr
        # removePunctuation does not replace with whitespace. Use a custom transformer ???
        #txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy = TRUE) #npnct<chr_ix>
        txt_corpus <- tm_map(txt_corpus, content_transformer(myreplacePunctuation)
                             , lazy = TRUE) #npnct<chr_ix>
#         txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))   
        if (!is.null(glb_txt_stop_words[[txt_var]]))
            txt_corpus <- tm_map(txt_corpus, removeWords, glb_txt_stop_words[[txt_var]],
                                 lazy = FALSE)#, lazy=TRUE) #nstopwrds

        # foreach result is based on .Last.Eval
        txt_corpus <- txt_corpus
        # glb_txt_corpus_lst[[txt_var]] <- txt_corpus
    }
    names(glb_txt_corpus_lst) <- glbFeatsText
    
mycombineSynonyms <- content_transformer(function(x, syn=NULL) { 
    Reduce(function(a,b) {
        gsub(paste0("\\b(", paste(b$syns, collapse = "|"),")\\b"), b$word, a)}, syn, x)   
})    
    
#stop(here"); glb_to_sav(); sav_txt_corpus <- glb_txt_corpus_lst[[txt_var]]; all.equal(sav_txt_corpus, glb_txt_corpus_lst[[txt_var]]); glb_txt_corpus_lst[[txt_var]] <- sav_txt_corpus
    glb_post_stop_words_terms_df_lst <- list(); 
    glb_post_stop_words_terms_mtrx_lst <- list();     
    glb_post_stem_words_terms_df_lst <- list(); 
    glb_post_stem_words_terms_mtrx_lst <- list();     
    for (txt_var in glbFeatsText) {
        print(sprintf("    Top_n post-stop term weights for %s:", txt_var))
        # This impacts stemming probably due to lazy parameter
        print(myprint_df(full_terms_df <-
                             get_corpus_terms(txt_corpus=glb_txt_corpus_lst[[txt_var]]), 
                        glbFeatsTextTermsMax[[txt_var]]))
        glb_post_stop_words_terms_df_lst[[txt_var]] <- full_terms_df
        terms_stop_mtrx <- as.matrix(DocumentTermMatrix(glb_txt_corpus_lst[[txt_var]], 
                                        control=glb_txt_terms_control))
        rownames(terms_stop_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
        glb_post_stop_words_terms_mtrx_lst[[txt_var]] <- terms_stop_mtrx
        
        tmp_allobs_df <- glb_allobs_df[, c(glb_id_var, glb_rsp_var)]
        tmp_allobs_df$terms.post.stop.n <- rowSums(terms_stop_mtrx > 0)
        tmp_allobs_df$terms.post.stop.n.log <- log(1 + tmp_allobs_df$terms.post.stop.n)
        tmp_allobs_df$weight.post.stop.sum <- rowSums(terms_stop_mtrx)        
        
        print(sprintf("    Top_n stem term weights for %s:", txt_var))        
        glb_txt_corpus_lst[[txt_var]] <- tm_map(glb_txt_corpus_lst[[txt_var]], stemDocument,
                                            "english", lazy=FALSE)
        if (!is.null(glb_txt_synonyms[[txt_var]])) {
            syn_lst <- myrmNullObj(glb_txt_synonyms[[txt_var]])
            glb_txt_corpus_lst[[txt_var]] <- tm_map(glb_txt_corpus_lst[[txt_var]],
                                                    mycombineSynonyms,
                                                    syn_lst, lazy=FALSE)
        }    
        
        print(myprint_df(full_terms_df <- get_corpus_terms(glb_txt_corpus_lst[[txt_var]]), 
                   glbFeatsTextTermsMax[[txt_var]]))
        glb_post_stem_words_terms_df_lst[[txt_var]] <- full_terms_df        
        terms_stem_mtrx <- as.matrix(DocumentTermMatrix(glb_txt_corpus_lst[[txt_var]], 
                                        control=glb_txt_terms_control))
        rownames(terms_stem_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
        glb_post_stem_words_terms_mtrx_lst[[txt_var]] <- terms_stem_mtrx
        
        tmp_allobs_df$terms.post.stem.n <- rowSums(terms_stem_mtrx > 0)
        tmp_allobs_df$terms.post.stem.n.log <- log(1 + tmp_allobs_df$terms.post.stem.n)
        tmp_allobs_df$weight.post.stem.sum <- rowSums(terms_stem_mtrx)
        
        tmp_allobs_df$terms.n.stem.stop.Ratio <- 
            1.0 * tmp_allobs_df$terms.post.stem.n / tmp_allobs_df$terms.post.stop.n
        tmp_allobs_df[(is.nan(tmp_allobs_df$terms.n.stem.stop.Ratio) | 
                       is.infinite(tmp_allobs_df$terms.n.stem.stop.Ratio)), 
                      "terms.n.stem.stop.Ratio"] <- 1.0                
        if ((n.errors <- sum(tmp_allobs_df$terms.n.stem.stop.Ratio > 1)) > 0)
            stop(n.errors, " obs in tmp_allobs_df have terms.n.stem.stop.Ratio > 1", 
                 " happening due to terms filtered by glb_txt_terms_control$bounds$global[1] but stemmable to other terms")
        #print(head(subset(tmp_allobs_df, terms.n.stem.stop.Ratio > 1)))
        #glb_allobs_df[(row_ix <- which(glb_allobs_df$UniqueID == 10465)), ]
        #terms_stop_mtrx[row_ix, terms_stop_mtrx[row_ix, ] > 0]
        #setdiff(names(terms_stem_mtrx[row_ix, terms_stem_mtrx[row_ix, ] > 0]), names(terms_stop_mtrx[row_ix, terms_stop_mtrx[row_ix, ] > 0]))
        #mydsp_obs(list(descr.my.contains="updat"))
        
        tmp_allobs_df$weight.sum.stem.stop.Ratio <- 
            1.0 * tmp_allobs_df$weight.post.stem.sum / tmp_allobs_df$weight.post.stop.sum
        tmp_allobs_df[is.nan(tmp_allobs_df$weight.sum.stem.stop.Ratio) | 
                      is.infinite(tmp_allobs_df$weight.sum.stem.stop.Ratio), 
                      "weight.sum.stem.stop.Ratio"] <- 1.0                
        
        tmp_trnobs_df <- tmp_allobs_df[!is.na(tmp_allobs_df[, glb_rsp_var]), ]
        print(cor(as.matrix(tmp_trnobs_df[, -c(1, 2)]), 
                  as.numeric(tmp_trnobs_df[, glb_rsp_var])))
        
        txt_var_pfx <- toupper(substr(txt_var, 1, 1))
        tmp_allobs_df <- tmp_allobs_df[, -c(1, 2)]
        names(tmp_allobs_df) <- paste(paste0(txt_var_pfx, "."), names(tmp_allobs_df), sep = "")
        glb_allobs_df <- cbind(glb_allobs_df, tmp_allobs_df)
        glbFeatsExclude <- c(glbFeatsExclude, 
                paste(paste0(txt_var_pfx, ".terms.post."), c("stop.n", "stem.n"), sep = ""))
    }
    
    require(wordcloud)
    for (txt_var in glbFeatsText) {
        print(sprintf("    Wordcloud post-stem term weights for %s:", txt_var))
        m <- glb_post_stem_words_terms_mtrx_lst[[txt_var]]
        # calculate the frequency of words
        v <- sort(colSums(m), decreasing = TRUE)
        myNames <- names(v)
        d <- data.frame(word = myNames, freq = v)
        wordcloud(d$word, d$freq, min.freq = glb_txt_terms_control$bounds$global[1])
    }    

    for (txt_var in glbFeatsText) {    
        .rnorm.cor.y.abs <- abs(cor(glb_allobs_df[glb_allobs_df$.src == "Train", ".rnorm"],
                    as.numeric(glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var]),
                                use = "pairwise.complete.obs"))
        plt_df <- subset(glb_post_stem_words_terms_df_lst[[txt_var]], !is.na(cor.y))
        plt_df$nzv.freqRatio.cut.fctr <- cut(plt_df$nzv.freqRatio, 
                                            breaks = sort(c(min(plt_df$nzv.freqRatio), 
                                                                glb_nzv_freqCut,
                                                            max(plt_df$nzv.freqRatio))))
        plt_df$nzv.percentUnique.cut.fctr <- cut(plt_df$nzv.percentUnique, 
                breaks = sort(c(min(plt_df$nzv.percentUnique) - .Machine$double.neg.eps, 
                                                            glb_nzv_uniqueCut,
                                                        max(plt_df$nzv.percentUnique))))
        plt_df$nzv.quad.fctr <- as.factor(paste0("fRatio:", plt_df$nzv.freqRatio.cut.fctr,
                                            "\n%Unq:", plt_df$nzv.percentUnique.cut.fctr))
        labelCnd <- !plt_df$nzv | 
                    (!is.na(plt_df$chisq.pval) & (plt_df$chisq.pval < 0.05)) & 
                (!is.na(plt_df$cor.y.abs) & (plt_df$cor.y.abs > .rnorm.cor.y.abs))
        plt_df$label <- NA; plt_df[labelCnd, "label"] <- plt_df[labelCnd, "term"]
        print(ggplot(plt_df, aes(x = cor.y, y = chisq.stat)) + 
                  geom_point(aes(color = nzv.quad.fctr, size = weight)) +
                  geom_text(aes(label = label), color = "gray50") + 
                  # geom_vline(xintercept = 0) + 
            geom_vline(xintercept = c(-1, +1) * .rnorm.cor.y.abs, color = "gray", 
                       linetype = "dashed") + 
            ggtitle(txt_var))
    }    

    extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, 
            paste0("extract.features_", "extract.DTM"), major.inc=TRUE)

#stop(here")    
    glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
    for (txt_var in glbFeatsText) {
        print(sprintf("Extracting term weights for %s...", txt_var))        
        txt_corpus <- glb_txt_corpus_lst[[txt_var]]
        
        full_DTM <- DocumentTermMatrix(txt_corpus, 
                                          control=glb_txt_terms_control)
        sprs_DTM <- removeSparseTerms(full_DTM, 
                                            glb_sprs_thresholds[txt_var])
        
        glb_full_DTM_lst[[txt_var]] <- full_DTM
        glb_sprs_DTM_lst[[txt_var]] <- sprs_DTM
    }

    extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, 
            paste0("extract.features_", "report.DTM"), major.inc=TRUE)

    require(reshape2)
    for (txt_var in glbFeatsText) {
        print(sprintf("Reporting term weights for %s...", txt_var))        
        full_DTM <- glb_full_DTM_lst[[txt_var]]
        sprs_DTM <- glb_sprs_DTM_lst[[txt_var]]        

        print("   Full TermMatrix:"); print(full_DTM)
        full_terms_df <- get_txt_terms(full_DTM)
#         full_terms_df <- full_terms_df[, c(2, 1, 3, 4)]
#         col_names <- names(full_terms_df)
#         col_names[2:length(col_names)] <- 
#             paste(col_names[2:length(col_names)], ".full", sep="")
#         names(full_terms_df) <- col_names

        print("   Sparse TermMatrix:"); print(sprs_DTM)
        sprs_terms_df <- get_txt_terms(sprs_DTM)
#         sprs_terms_df <- sprs_terms_df[, c(2, 1, 3, 4)]
#         col_names <- names(sprs_terms_df)
#         col_names[2:length(col_names)] <- 
#             paste(col_names[2:length(col_names)], ".sprs", sep="")
#         names(sprs_terms_df) <- col_names

        #intersect(names(full_terms_df), names(sprs_terms_df))
        terms_df <- merge(full_terms_df, sprs_terms_df, by = c("term", "weight", "freq",
                                        grep("weight\\.", names(full_terms_df), value = TRUE)),
                          all.x = TRUE, suffixes = c(".full", ".sprs"))
        terms_df$in.sprs <- !is.na(terms_df$pos.sprs)
        plt_terms_df <- subset(terms_df, 
                        weight >= min(terms_df$weight[!is.na(terms_df$pos.sprs)], na.rm=TRUE))
        plt_terms_df$label <- ""
        plt_terms_df[is.na(plt_terms_df$pos.sprs), "label"] <- 
            plt_terms_df[is.na(plt_terms_df$pos.sprs), "term"]
#         glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
#             plt_terms_df[is.na(plt_terms_df$TfIdf.sprs), "term"])
        print(myplot_scatter(plt_terms_df, "freq", "weight", 
                             colorcol_name="in.sprs") + 
                  geom_text(aes(label=label), color="Black", size=3.5))
        
        melt_terms_df <- orderBy(~ -value, 
                            melt(terms_df, id.vars="term", measure.vars = c("weight", "freq")))
        print(ggplot(melt_terms_df, aes(value, color=variable)) + stat_ecdf() + 
                  geom_hline(yintercept=glb_sprs_thresholds[txt_var], 
                             linetype = "dotted"))
        
        melt_terms_df <- orderBy(~ -value, 
                        melt(subset(terms_df, in.sprs), id.vars="term",
                             measure.vars=grep("weight.", names(terms_df), value=TRUE)))
        print(myplot_hbar(melt_terms_df, "term", "value", colorcol_name="variable"))
        
        melt_terms_df <- orderBy(~ -value, 
                        melt(subset(terms_df, !in.sprs), id.vars="term",
                             measure.vars=grep("weight.", names(terms_df), value=TRUE)))
        print(myplot_hbar(head(melt_terms_df, glbFeatsTextTermsMax[[txt_var]]), "term", "value",
                          colorcol_name="variable"))
    }

#     sav_full_DTM_lst <- glb_full_DTM_lst
#     print(identical(sav_glb_txt_corpus_lst, glb_txt_corpus_lst))
#     print(all.equal(length(sav_glb_txt_corpus_lst), length(glb_txt_corpus_lst)))
#     print(all.equal(names(sav_glb_txt_corpus_lst), names(glb_txt_corpus_lst)))
#     print(all.equal(sav_glb_txt_corpus_lst[["Headline"]], glb_txt_corpus_lst[["Headline"]]))

#     print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
        
    rm(full_terms_mtrx)

    # Create txt features
    if ((length(glbFeatsText) > 1) &&
        (length(unique(pfxs <- sapply(glbFeatsText, 
                    function(txt) toupper(substr(txt, 1, 1))))) < length(glbFeatsText)))
            stop("Prefixes for corpus freq terms not unique: ", pfxs)
    
    extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, 
                            paste0("extract.features_", "bind.DTM"), 
                                         major.inc=TRUE)
#stop(here"); glb_to_sav(); all.equal(sav_allobs_df, glb_allobs_df); glb_allobs_df <- sav_allobs_df
    require(tidyr)
    for (txt_var in glbFeatsText) {
        print(sprintf("Binding DTM for %s...", txt_var))
        txt_var_pfx <- toupper(substr(txt_var, 1, 1))
        
        txt_full_X_df <- as.data.frame(as.matrix(glb_full_DTM_lst[[txt_var]]))
        terms_full_df <- get_txt_terms(glb_full_DTM_lst[[txt_var]])     
        # make.names adds a period to R keywords e.g. "in", "function"
        colnames(txt_full_X_df) <- paste(txt_var_pfx, ".T.",
                                    make.names(colnames(txt_full_X_df)), sep="")
        rownames(txt_full_X_df) <- rownames(glb_allobs_df) # warning otherwise
        
#         plt_full_df <- terms_full_df
#         names(plt_full_df)[grepl("weight$", names(plt_full_df))] <- "weight.all"
#     #     gather(plt_full_df[1:5, ], domain, TfIdf, -matches("!(TfIdf)"))
#     #     gather(plt_full_df[1:5, grepl("TfIdf", names(plt_full_df))], domain, TfIdf) 
#     #     gather(plt_full_df[1:5, ], domain, TfIdf, 
#     #            -names(plt_full_df)[!grepl("TfIdf", names(plt_full_df))]) 
#         plt_full_df <- gather(plt_full_df, domain, weight, 
#                               -c(term, freq, pos, cor.y, cor.y.abs))
#         plt_full_df$label <- NA
#         top_val_terms <- orderBy(~-weight, terms_full_df)$term[1:glbFeatsTextTermsMax[[txt_var]]]
#         plt_full_df[plt_full_df$term %in% top_val_terms, "label"] <- 
#             plt_full_df[plt_full_df$term %in% top_val_terms, "term"]
#         top_cor_terms <- orderBy(~-cor.y.abs,
#                                  terms_full_df)$term[1:glbFeatsTextTermsMax[[txt_var]]]
#         plt_full_df[plt_full_df$term %in% top_cor_terms, "label"] <- 
#             plt_full_df[plt_full_df$term %in% top_cor_terms, "term"]
#         #plt_full_df$type <- "none"
#         plt_full_df[plt_full_df$term %in% top_val_terms, "type"] <- "top.weight" 
#         plt_full_df[plt_full_df$term %in% top_cor_terms, "type"] <- "top.cor"
#         plt_full_df[plt_full_df$term %in% intersect(top_val_terms, top_cor_terms), "type"] <-
#             "top.both"
#         cor.y.rnorm <- cor(glb_allobs_df$.rnorm, as.numeric(glb_allobs_df[, glb_rsp_var]),
#                            use = "pairwise.complete.obs")
#         print(ggplot(plt_full_df, aes(x=weight, y=cor.y)) + facet_wrap(~ domain) + 
#                 geom_point(aes(size=freq), color="grey") + 
#                 geom_jitter() + 
#                 geom_text(aes(label=label, color=type), size=3.5) +
#         #geom_hline(yintercept=cor.y.rnorm, color="red") + 
#         geom_hline(yintercept=c(cor.y.rnorm, -cor.y.rnorm), color="red"))

#stop(here"); glb_to_sav()        
        if (glbFeatsTextFilter == "sparse") {
            txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
            select_terms <- make.names(colnames(txt_X_df))
#             colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
#                                         make.names(colnames(txt_X_df)), sep="")
#             rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
        } else if (glbFeatsTextFilter == "top.val") {
            select_terms <- orderBy(~-weight,
                                    terms_full_df)$term[1:glbFeatsTextTermsMax[[txt_var]]]
#             txt_X_df <- txt_full_X_df[, subset(terms_full_df, term %in% select_terms)$pos,
#                                       FALSE]
        } else if (glbFeatsTextFilter == "top.cor") {
            select_terms <- orderBy(~-cor.y.abs,
                                    terms_full_df)$term[1:glbFeatsTextTermsMax[[txt_var]]]
#             txt_X_df <- txt_full_X_df[, subset(terms_full_df, term %in% select_terms)$pos,
#                                       FALSE]
        } else if (glbFeatsTextFilter == "top.chisq") {
            select_terms <- orderBy(~-chisq.stat,
                                    subset(terms_full_df, chisq.pval < 0.05)
                                    )$term[1:glbFeatsTextTermsMax[[txt_var]]]
        } else if (glbFeatsTextFilter == "union.top.val.cor") {
            select_terms <- union(
                orderBy(~-weight   , terms_full_df)$term[1:glbFeatsTextTermsMax[[txt_var]]],
                orderBy(~-cor.y.abs, terms_full_df)$term[1:glbFeatsTextTermsMax[[txt_var]]])
        } else stop(
        "glbFeatsTextFilter should be one of c('sparse', 'top.val', 'top.cor', 'union.top.val.cor', 'top.chisq') vs. '",
                    glbFeatsTextFilter, "'")    
        
        assoc_terms_lst <- findAssocs(glb_full_DTM_lst[[txt_var]], select_terms, 
                                      glbFeatsTextAssocCor[[txt_var]])
        assoc_terms <- c(NULL)
        for (term in names(assoc_terms_lst))
            if (length(assoc_terms_lst[[term]]) > 0)
                assoc_terms <- union(assoc_terms, names(assoc_terms_lst[[term]]))

#stop(here"); glb_to_sav()
        txt_X_df <- txt_full_X_df[, 
                        subset(terms_full_df, term %in% c(select_terms, assoc_terms))$pos,
                                    FALSE]
        glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
        #glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics 
    }
    #identical(chk_entity_df, glb_allobs_df)
    #chk_entity_df <- glb_allobs_df

    extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, 
                            paste0("extract.features_", "bind.DXM"), 
                                         major.inc=TRUE)

#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
    glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'", 
                        "\\(|\\)",# "\\(", "\\)", 
                        "\\*", "\\+", ",", "-", "\\.", "/", ":", ";", 
                        "<|>", # "<", 
                        "=", 
                        # ">", 
                        "\\?", "@", "\\[", "\\\\", "\\]", "\\^", "_", "`", 
                        "\\{", "\\|", "\\}", "~")
    txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
    txt_X_df <- foreach(txt_var=glbFeatsText, .combine=cbind) %dopar% {   
    #for (txt_var in glbFeatsText) {
        print(sprintf("Binding DXM for %s...", txt_var))
        txt_var_pfx <- toupper(substr(txt_var, 1, 1))        

        txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
        rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
        #print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
        
        # Create <txt_var>.T.<term> for glb_important_terms
        for (term in glb_important_terms[[txt_var]])
            txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <- 
                txt_full_DTM_mtrx[, term]
                
        # Create <txt_var>.wrds.n.log & .wrds.unq.n.log
        txt_X_df[, paste0(txt_var_pfx, ".wrds.n.log")] <- 
            log(1 + mycount_pattern_occ("\\w+", glb_txt_chr_lst[[txt_var]]))
        txt_X_df[, paste0(txt_var_pfx, ".wrds.unq.n.log")] <- 
            log(1 + rowSums(txt_full_DTM_mtrx != 0))
        txt_X_df[, paste0(txt_var_pfx, ".weight.sum")] <- 
            rowSums(txt_full_DTM_mtrx) 
        txt_X_df[, paste0(txt_var_pfx, ".ratio.weight.sum.wrds.n")] <- 
            txt_X_df[, paste0(txt_var_pfx, ".weight.sum")] / 
            (exp(txt_X_df[, paste0(txt_var_pfx, ".wrds.n.log")]) - 1)
        txt_X_df[is.nan(txt_X_df[, paste0(txt_var_pfx, ".ratio.weight.sum.wrds.n")]),
                 paste0(txt_var_pfx, ".ratio.weight.sum.wrds.n")] <- 0

        # Create <txt_var>.chrs.n.log
        txt_X_df[, paste0(txt_var_pfx, ".chrs.n.log")] <- 
            log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
        txt_X_df[, paste0(txt_var_pfx, ".chrs.uppr.n.log")] <- 
            log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
        txt_X_df[, paste0(txt_var_pfx, ".dgts.n.log")] <- 
            log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))

        # Create <txt_var>.npnct?.log
        # would this be faster if it's iterated over each row instead of 
        #   each created column ???
        for (punct_ix in 1:length(glb_punct_vctr)) { 
#             smp0 <- " "
#             smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
#             smp2 <- paste(smp1, smp1, sep=" ")
#             print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix])) 
#             results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
#             names(results) <- NULL; print(results)
            txt_X_df[, 
                paste0(txt_var_pfx, ".chrs.pnct", sprintf("%02d", punct_ix), ".n.log")] <-
                log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix], 
                                            glb_allobs_df[, txt_var]))
        }
#         print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0, 
#                                     c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))    
        
        # Create <txt_var>.wrds.stop.n.log & <txt_var>ratio.wrds.stop.n.wrds.n
        if (!is.null(glb_txt_stop_words[[txt_var]])) {
            stop_words_rex_str <- paste0("\\b(", 
                                         paste0(glb_txt_stop_words[[txt_var]], collapse="|"),
                                         ")\\b")
            txt_X_df[, paste0(txt_var_pfx, ".wrds.stop.n", ".log")] <-
                log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_chr_lst[[txt_var]]))
            txt_X_df[, paste0(txt_var_pfx, ".ratio.wrds.stop.n.wrds.n")] <-
                exp(txt_X_df[, paste0(txt_var_pfx, ".wrds.stop.n", ".log")] - 
                    txt_X_df[, paste0(txt_var_pfx, ".wrds.n", ".log")])
        }

        # Create <txt_var>.P.http
        txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <- 
            as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))    
    
        # Create <txt_var>.P.mini & air
        txt_X_df[, paste(txt_var_pfx, ".P.mini", sep="")] <- 
            as.integer(0 + mycount_pattern_occ("mini(?!m)", glb_allobs_df[, txt_var],
                                               perl=TRUE))    
        txt_X_df[, paste(txt_var_pfx, ".P.air", sep="")] <- 
            as.integer(0 + mycount_pattern_occ("(?<![fhp])air", glb_allobs_df[, txt_var],
                                               perl=TRUE))    
        txt_X_df[, paste(txt_var_pfx, ".P.black", sep="")] <- 
            as.integer(0 + mycount_pattern_occ("black", glb_allobs_df[, txt_var],
                                               perl=TRUE))    
        txt_X_df[, paste(txt_var_pfx, ".P.white", sep="")] <- 
            as.integer(0 + mycount_pattern_occ("white", glb_allobs_df[, txt_var],
                                               perl=TRUE))    
        txt_X_df[, paste(txt_var_pfx, ".P.gold", sep="")] <- 
            as.integer(0 + mycount_pattern_occ("gold", glb_allobs_df[, txt_var],
                                               perl=TRUE))    
        txt_X_df[, paste(txt_var_pfx, ".P.spacegray", sep="")] <- 
            as.integer(0 + mycount_pattern_occ("spacegray", glb_allobs_df[, txt_var],
                                               perl=TRUE))    
    
        txt_X_df <- subset(txt_X_df, select=-.rnorm)
        txt_X_df <- txt_X_df[, -grep(glb_id_var, names(txt_X_df), fixed=TRUE), FALSE]
        #glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
    }
    glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
    #myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
    
#     if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
#         stop("Why is this happening ?")

    # Generate summaries
#     print(summary(glb_allobs_df))
#     print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
#     print(summary(glb_trnobs_df))
#     print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
#     print(summary(glb_newobs_df))
#     print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))

    glbFeatsExclude <- union(glbFeatsExclude, 
                                          glbFeatsText)
    rm(log_X_df, txt_X_df)
}
## Loading required package: stringr
##                                 label step_major step_minor label_minor
## 2 extract.features_factorize.str.vars          2          0           0
## 3       extract.features_process.text          3          0           0
##      bgn    end elapsed
## 2 47.074 47.144   0.071
## 3 47.145     NA      NA
## [1] "Building glb_txt_chr_lst..."
## [1] "running gsub for 10 (of 179): #\\bCentral African Republic\\b#..."
## [1] "running gsub for 20 (of 179): #\\bAlejandro G\\. I&ntilde;&aacute;rritu#..."
## [1] "running gsub for 30 (of 179): #\\bC\\.A\\.A\\.#..."
## [1] "running gsub for 40 (of 179): #\\bCV\\.#..."
## [1] "running gsub for 50 (of 179): #\\bE\\.P\\.A\\.#..."
## [1] "running gsub for 60 (of 179): #\\bG\\.I\\. Joe#..."
## [1] "running gsub for 70 (of 179): #\\bISIS\\.#..."
## [1] "running gsub for 80 (of 179): #\\bJ\\.K\\. Simmons#..."
## [1] "running gsub for 90 (of 179): #\\bM\\. Henri Pol#..."
## [1] "running gsub for 100 (of 179): #\\bN\\.Y\\.S\\.E\\.#..."
## [1] "running gsub for 110 (of 179): #\\bR\\.B\\.S\\.#..."
## [1] "running gsub for 120 (of 179): #\\bSteven A\\. Cohen#..."
## [1] "running gsub for 130 (of 179): #\\bV\\.A\\.#..."
## [1] "running gsub for 140 (of 179): #\\bWall Street#..."
## [1] "running gsub for 150 (of 179): #\\bSaint( |-)((Laurent|Lucia)\\b)+#..."
## [1] "running gsub for 160 (of 179): #\\bSouth( |\\\\.)(America|American|Africa|African|Carolina|Dakota|Korea|Korean|Sudan)\\b#..."
## [1] "running gsub for 170 (of 179): #(\\w)-a-year#..."
## [1] "Remaining OK in descr.my:"
##   pattern .n
## 1      OK  6
## [[1]]
## [1] NA
## attr(,"match.length")
## [1] NA
## 
## [1] NA
## [[1]]
## [1] NA
## attr(,"match.length")
## [1] NA
## 
## [1] NA
## [[1]]
## [1] NA
## attr(,"match.length")
## [1] NA
## 
## [1] NA
## [[1]]
## [1] NA
## attr(,"match.length")
## [1] NA
## 
## [1] NA
## [[1]]
## [1] NA
## attr(,"match.length")
## [1] NA
## 
## [1] NA
## [[1]]
## [1] NA
## attr(,"match.length")
## [1] NA
## 
## [1] NA
## [1] pattern .n     
## <0 rows> (or 0-length row.names)
## [1] pattern .n     
## <0 rows> (or 0-length row.names)
## [1] "Remaining Acronyms in descr.my:"
## [1] pattern .n     
## <0 rows> (or 0-length row.names)
##        pattern .n
## 1  CONDITION.   6
## 2        ONLY.  5
## 3         GB.   4
## 4       BOX.    2
## 5     CORNER.   2
## 6         ESN.  2
## 7       GOOD.   2
## 8      ICLOUD.  2
## 9        IMEI.  2
## 10      IPADS.  2
## 11    LOCKED.   2
## 12      LOCKS.  2
## 13         ON.  2
## 14 SCRATCHES.   2
## 15    TEARS.    2
## 16       USE.   2
## 17   WIFIONLY.  2
## [1] "Remaining #\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+# terms in descr.my: "
##         pattern .n
## 1      New Open  3
## 4 New Digitizer  1
## 5    New Opened  1
## 6    New Screen  1
## [1] "    consider cleaning if relevant to problem domain; geography name; .n > 1"
## [1] "Remaining #\\b(N|S|E|W|C)( |\\.)(\\w)+# terms in descr.my: "
##   pattern .n
## 1 C Stock  3
## 2  W blue  1
## [1] "Remaining #\\b(North|South|East|West|Central)( |\\.)(\\w)+# terms in descr.my: "
##                                                    label step_major
## 3                          extract.features_process.text          3
## 4 extract.features_process.text_reporting_compound_terms          3
##   step_minor label_minor    bgn    end elapsed
## 3          0           0 47.145 48.734   1.589
## 4          1           1 48.735     NA      NA
##                                                    label step_major
## 4 extract.features_process.text_reporting_compound_terms          3
## 5                          extract.features_build.corpus          4
##   step_minor label_minor    bgn   end elapsed
## 4          1           1 48.735 48.74   0.005
## 5          0           0 48.741    NA      NA
## [1] "Building glb_txt_corpus_lst..."
## [1] "    Top_n post-stop term weights for descr.my:"
## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact
## Warning in cor(docms_mtrx[glb_allobs_df$.src == "Train", ],
## as.numeric(glb_allobs_df[glb_allobs_df$.src == : the standard deviation is
## zero
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## [1] "Rows: 347; Cols: 14"
##                term    weight freq pos        cor.y   cor.y.abs chisq.stat
## condition condition 160.57765  486  69 -0.034976682 0.034976682   69.49175
## likenew     likenew 123.78010   70 175 -0.043168766 0.043168766   22.07653
## in               in 102.43546  432 146 -0.071653919 0.071653919   40.30764
## used           used  96.66889  229 327  0.008747051 0.008747051   31.92471
## and             and  92.37461  331  21  0.006010357 0.006010357   38.45444
## is               is  87.72451  335 162 -0.042931491 0.042931491   26.34374
##             chisq.pval nzv.freqRatio nzv.percentUnique  nzv weight.N
## condition 2.609830e-06      36.97561          1.351351 TRUE 61.61577
## likenew   1.817939e-01     257.71429          0.972973 TRUE 54.11495
## in        1.981535e-02      33.52174          1.351351 TRUE 46.12932
## used      1.017193e-01      54.54839          1.297297 TRUE 33.84672
## and       7.095553e-02      55.24138          1.513514 TRUE 35.73653
## is        5.541564e-01      54.06667          1.567568 TRUE 35.26992
##           weight.Y weight.NA
## condition 42.65757  56.30431
## likenew   19.52427  50.14088
## in        26.82731  29.47884
## used      31.76561  31.05656
## and       31.78712  24.85096
## is        23.45595  28.99864
##                term    weight freq pos        cor.y   cor.y.abs chisq.stat
## case           case 33.533127   76  54 -0.009225083 0.009225083  16.106811
## protector protector 10.206604   18 253  0.006965022 0.006965022   8.315515
## contact     contact 10.006409   15  71  0.035032399 0.035032399   9.951124
## 64gb           64gb  8.260734   14  10 -0.030822325 0.030822325   8.352751
## otterbox   otterbox  7.546463   10 221 -0.013289954 0.013289954   4.917015
## look           look  1.267528    2 182 -0.030370111 0.030370111   1.720453
##           chisq.pval nzv.freqRatio nzv.percentUnique  nzv  weight.N
## case       0.5850931      198.4444         1.0270270 TRUE 16.019773
## protector  0.5026891      611.6667         0.5405405 TRUE  4.336370
## contact    0.0412591      262.5714         0.2702703 TRUE  2.440087
## 64gb       0.3025184      613.6667         0.4324324 TRUE  4.043603
## otterbox   0.5544999      921.5000         0.3783784 TRUE  3.401559
## look       0.4230663     1848.0000         0.1621622 TRUE  1.267528
##            weight.Y weight.NA
## case      12.327119  5.186234
## protector  4.386232  1.484003
## contact    5.843907  1.722415
## 64gb       1.260555  2.956575
## otterbox   1.826941  2.317964
## look       0.000000  0.000000
##                  term    weight freq pos       cor.y  cor.y.abs
## having         having 0.7106680    1 140          NA         NA
## shipped       shipped 0.7106680    1 284  0.02508765 0.02508765
## thats           thats 0.6688640    1 310 -0.02155773 0.02155773
## gentle         gentle 0.6317049    1 130 -0.02155773 0.02155773
## opening       opening 0.6317049    1 216  0.02508765 0.02508765
## previously previously 0.5984572    1 246          NA         NA
##              chisq.stat chisq.pval nzv.freqRatio nzv.percentUnique  nzv
## having               NA         NA             0        0.05405405 TRUE
## shipped    5.762908e-03  0.9394877          1849        0.10810811 TRUE
## thats      1.281078e-27  1.0000000          1849        0.10810811 TRUE
## gentle     1.281078e-27  1.0000000          1849        0.10810811 TRUE
## opening    5.762908e-03  0.9394877          1849        0.10810811 TRUE
## previously           NA         NA             0        0.05405405 TRUE
##             weight.N  weight.Y weight.NA
## having     0.0000000 0.0000000 0.7106680
## shipped    0.0000000 0.7106680 0.0000000
## thats      0.6688640 0.0000000 0.0000000
## gentle     0.6317049 0.0000000 0.0000000
## opening    0.0000000 0.6317049 0.0000000
## previously 0.0000000 0.0000000 0.5984572
##                  term    weight freq pos       cor.y  cor.y.abs
## having         having 0.7106680    1 140          NA         NA
## shipped       shipped 0.7106680    1 284  0.02508765 0.02508765
## thats           thats 0.6688640    1 310 -0.02155773 0.02155773
## gentle         gentle 0.6317049    1 130 -0.02155773 0.02155773
## opening       opening 0.6317049    1 216  0.02508765 0.02508765
## previously previously 0.5984572    1 246          NA         NA
##              chisq.stat chisq.pval nzv.freqRatio nzv.percentUnique  nzv
## having               NA         NA             0        0.05405405 TRUE
## shipped    5.762908e-03  0.9394877          1849        0.10810811 TRUE
## thats      1.281078e-27  1.0000000          1849        0.10810811 TRUE
## gentle     1.281078e-27  1.0000000          1849        0.10810811 TRUE
## opening    5.762908e-03  0.9394877          1849        0.10810811 TRUE
## previously           NA         NA             0        0.05405405 TRUE
##             weight.N  weight.Y weight.NA
## having     0.0000000 0.0000000 0.7106680
## shipped    0.0000000 0.7106680 0.0000000
## thats      0.6688640 0.0000000 0.0000000
## gentle     0.6317049 0.0000000 0.0000000
## opening    0.0000000 0.6317049 0.0000000
## previously 0.0000000 0.0000000 0.5984572
## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact
## [1] "    Top_n stem term weights for descr.my:"
## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact
## Warning in cor(docms_mtrx[glb_allobs_df$.src == "Train", ],
## as.numeric(glb_allobs_df[glb_allobs_df$.src == : the standard deviation is
## zero
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## [1] "Rows: 226; Cols: 14"
##            term    weight freq pos        cor.y   cor.y.abs chisq.stat
## condit   condit 160.55353  492  52 -0.033248924 0.033248924   68.76178
## likenew likenew 123.78010   70 117 -0.043168766 0.043168766   22.07653
## use         use 111.06226  285 210  0.003025811 0.003025811   45.28121
## in           in 102.43546  432  98 -0.071653919 0.071653919   40.30764
## and         and  92.37461  331  20  0.006010357 0.006010357   38.45444
## is           is  87.72451  335 110 -0.042931491 0.042931491   26.34374
##           chisq.pval nzv.freqRatio nzv.percentUnique  nzv weight.N
## condit  3.361980e-06      36.82927          1.351351 TRUE 61.51671
## likenew 1.817939e-01     257.71429          0.972973 TRUE 54.11495
## use     1.518411e-02      86.63158          1.513514 TRUE 40.77891
## in      1.981535e-02      33.52174          1.351351 TRUE 46.12932
## and     7.095553e-02      55.24138          1.513514 TRUE 35.73653
## is      5.541564e-01      54.06667          1.567568 TRUE 35.26992
##         weight.Y weight.NA
## condit  43.14002  55.89681
## likenew 19.52427  50.14088
## use     35.93377  34.34958
## in      26.82731  29.47884
## and     31.78712  24.85096
## is      23.45595  28.99864
##              term    weight freq pos        cor.y   cor.y.abs chisq.stat
## brandnew brandnew 37.154496   30  36 -0.014982381 0.014982381  13.498032
## doesnt     doesnt 25.357692   57  67 -0.052798870 0.052798870  20.355151
## never       never 11.829767   15 132 -0.003422891 0.003422891   7.655336
## set           set 10.427103   13 178  0.035657781 0.035657781   8.724937
## restor     restor  9.331353   13 168 -0.005404144 0.005404144   9.811595
## absolut   absolut  8.451809   12  12  0.066008565 0.066008565   9.350376
##          chisq.pval nzv.freqRatio nzv.percentUnique  nzv  weight.N
## brandnew 0.56388835      609.0000         0.8648649 TRUE 21.081563
## doesnt   0.06065895      129.4286         0.7027027 TRUE 11.924374
## never    0.36396438      459.7500         0.4324324 TRUE  4.776183
## set      0.18964858      920.0000         0.3783784 TRUE  2.246840
## restor   0.19950403      613.3333         0.4324324 TRUE  4.584716
## absolut  0.15481081      921.0000         0.3783784 TRUE  0.000000
##           weight.Y weight.NA
## brandnew 11.651061  4.421872
## doesnt    4.248782  9.184536
## never     3.700799  3.352785
## set       5.534968  2.645295
## restor    3.357971  1.388666
## absolut   6.251752  2.200057
##            term   weight freq pos         cor.y    cor.y.abs   chisq.stat
## 4g           4g 2.200200    3   9  0.0002965679 0.0002965679 2.023052e+00
## sprint   sprint 2.037099    2 191 -0.0215577316 0.0215577316 1.281078e-27
## verizon verizon 2.037099    2 212 -0.0215577316 0.0215577316 1.281078e-27
## tmobil   tmobil 1.893062    2 203  0.0094480109 0.0094480109 2.023052e+00
## ipad1     ipad1 1.834814    2 101  0.0067428569 0.0067428569 2.023052e+00
## gold       gold 1.137069    1  86 -0.0215577316 0.0215577316 1.281078e-27
##         chisq.pval nzv.freqRatio nzv.percentUnique  nzv  weight.N
## 4g       0.3636637          1848         0.1621622 TRUE 0.6989804
## sprint   1.0000000          1849         0.1081081 TRUE 0.7407634
## verizon  1.0000000          1849         0.1081081 TRUE 0.7407634
## tmobil   0.3636637          1848         0.1621622 TRUE 0.7407634
## ipad1    0.3636637          1848         0.1621622 TRUE 0.7977452
## gold     1.0000000          1849         0.1081081 TRUE 1.1370687
##          weight.Y weight.NA
## 4g      0.6116078 0.8896114
## sprint  0.0000000 1.2963359
## verizon 0.0000000 1.2963359
## tmobil  1.1522986 0.0000000
## ipad1   1.0370687 0.0000000
## gold    0.0000000 0.0000000
##            term   weight freq pos         cor.y    cor.y.abs   chisq.stat
## 4g           4g 2.200200    3   9  0.0002965679 0.0002965679 2.023052e+00
## sprint   sprint 2.037099    2 191 -0.0215577316 0.0215577316 1.281078e-27
## verizon verizon 2.037099    2 212 -0.0215577316 0.0215577316 1.281078e-27
## tmobil   tmobil 1.893062    2 203  0.0094480109 0.0094480109 2.023052e+00
## ipad1     ipad1 1.834814    2 101  0.0067428569 0.0067428569 2.023052e+00
## gold       gold 1.137069    1  86 -0.0215577316 0.0215577316 1.281078e-27
##         chisq.pval nzv.freqRatio nzv.percentUnique  nzv  weight.N
## 4g       0.3636637          1848         0.1621622 TRUE 0.6989804
## sprint   1.0000000          1849         0.1081081 TRUE 0.7407634
## verizon  1.0000000          1849         0.1081081 TRUE 0.7407634
## tmobil   0.3636637          1848         0.1621622 TRUE 0.7407634
## ipad1    0.3636637          1848         0.1621622 TRUE 0.7977452
## gold     1.0000000          1849         0.1081081 TRUE 1.1370687
##          weight.Y weight.NA
## 4g      0.6116078 0.8896114
## sprint  0.0000000 1.2963359
## verizon 0.0000000 1.2963359
## tmobil  1.1522986 0.0000000
## ipad1   1.0370687 0.0000000
## gold    0.0000000 0.0000000
## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact
##                                    [,1]
## terms.post.stop.n          -0.075731613
## terms.post.stop.n.log      -0.062526435
## weight.post.stop.sum       -0.045009208
## terms.post.stem.n          -0.074990216
## terms.post.stem.n.log      -0.062220631
## weight.post.stem.sum       -0.047105442
## terms.n.stem.stop.Ratio     0.043562113
## weight.sum.stem.stop.Ratio  0.002381527
## Loading required package: wordcloud
## Loading required package: RColorBrewer
## [1] "    Wordcloud post-stem term weights for descr.my:"
## Warning in wordcloud(d$word, d$freq, min.freq = glb_txt_terms_control
## $bounds$global[1]): likenew could not be fit on page. It will not be
## plotted.

## Warning: Removed 195 rows containing missing values (geom_text).
##                           label step_major step_minor label_minor    bgn
## 5 extract.features_build.corpus          4          0           0 48.741
## 6  extract.features_extract.DTM          5          0           0 71.465
##      end elapsed
## 5 71.464  22.723
## 6     NA      NA
## [1] "Extracting term weights for descr.my..."
## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact
##                          label step_major step_minor label_minor    bgn
## 6 extract.features_extract.DTM          5          0           0 71.465
## 7  extract.features_report.DTM          6          0           0 72.831
##      end elapsed
## 6 72.831   1.366
## 7     NA      NA
## [1] "Reporting term weights for descr.my..."
## [1] "   Full TermMatrix:"
## <<DocumentTermMatrix (documents: 2648, terms: 226)>>
## Non-/sparse entries: 11341/587107
## Sparsity           : 98%
## Maximal term length: 10
## Weighting          : term frequency - inverse document frequency (normalized) (tf-idf)
## Warning in cor(docms_mtrx[glb_allobs_df$.src == "Train", ],
## as.numeric(glb_allobs_df[glb_allobs_df$.src == : the standard deviation is
## zero
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## [1] "   Sparse TermMatrix:"
## <<DocumentTermMatrix (documents: 2648, terms: 19)>>
## Non-/sparse entries: 4701/45611
## Sparsity           : 91%
## Maximal term length: 7
## Weighting          : term frequency - inverse document frequency (normalized) (tf-idf)
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in myplot_scatter(plt_terms_df, "freq", "weight", colorcol_name =
## "in.sprs"): converting in.sprs to class:factor

## Warning in rm(full_terms_mtrx): object 'full_terms_mtrx' not found
##                         label step_major step_minor label_minor    bgn
## 7 extract.features_report.DTM          6          0           0 72.831
## 8   extract.features_bind.DTM          7          0           0 77.562
##      end elapsed
## 7 77.561   4.731
## 8     NA      NA
## Loading required package: tidyr
## [1] "Binding DTM for descr.my..."
## Warning in cor(docms_mtrx[glb_allobs_df$.src == "Train", ],
## as.numeric(glb_allobs_df[glb_allobs_df$.src == : the standard deviation is
## zero
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect
##                       label step_major step_minor label_minor    bgn
## 8 extract.features_bind.DTM          7          0           0 77.562
## 9 extract.features_bind.DXM          8          0           0 80.382
##      end elapsed
## 8 80.382    2.82
## 9     NA      NA
## [1] "Binding DXM for descr.my..."
## Warning in rm(log_X_df, txt_X_df): object 'log_X_df' not found

# Use model info provided in description
# mydsp_obs(list(description.contains="a[[:digit:]]"), cols=glb_dsp_cols, all=TRUE)
# glb_allobs_df[glb_allobs_df$UniqueID == 12474, "prdline.my"] <- "iPad mini"
# glb_allobs_df[glb_allobs_df$UniqueID == 12474, "color"] <- "Space Gray"
# glb_allobs_df[glb_allobs_df$UniqueID == 12474, "cellular"] <- "0"
# glb_allobs_df[glb_allobs_df$UniqueID == 12474, "carrier"] <- "None"
# 
# mydsp_obs(list(description.contains="m(.{4})ll"), cols=glb_dsp_cols, all=TRUE)
# glb_allobs_df[glb_allobs_df$UniqueID == 11360, "color"] <- "Black"
# glb_allobs_df[glb_allobs_df$UniqueID == 11360, "storage"] <- "64"
# glb_allobs_df[glb_allobs_df$UniqueID == 11360, "cellular"] <- "0"
# glb_allobs_df[glb_allobs_df$UniqueID == 11360, "carrier"] <- "None"
# 
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "prdline.my"] <- "iPad Air"
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "storage"] <- "32"
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "color"] <- "White"
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "cellular"] <- "0"
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "carrier"] <- "None"

# mydsp_obs(list(description.contains="mini(?!m)"), perl=TRUE, cols="D.P.mini", all=TRUE)
# mydsp_obs(list(D.P.mini=1), cols="D.P.mini", all=TRUE)
# mydsp_obs(list(D.P.mini=1, productline="Unknown"), cols="D.P.mini", all=TRUE)

# mydsp_obs(list(description.contains="(?<![fhp])air"), perl=TRUE, all=TRUE)
# mydsp_obs(list(description.contains="air"), perl=FALSE, cols="D.P.air", all=TRUE)
# mydsp_obs(list(D.P.air=1, productline="Unknown"), cols="D.P.air", all=TRUE)

# print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.mini",
#                                            glb_rsp_var)))
# print(glb_allobs_df[(glb_allobs_df$productline == "Unknown") & 
#                     (glb_allobs_df$D.P.mini > 0), 
#                     c(glb_id_var, glb_category_var, glb_dsp_cols, glbFeatsText)])
# glb_allobs_df[(glb_allobs_df$D.P.mini == 1) & (glb_allobs_df$productline == "Unknown"),
#               "prdline.my"] <- "iPad mini"

# print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.air",
#                                            glb_rsp_var)))
# print(glb_allobs_df[(glb_allobs_df$productline == "Unknown") & 
#                     (glb_allobs_df$D.P.air > 0), 
#                     c(glb_id_var, glb_category_var, glb_dsp_cols, glbFeatsText)])
# #glb_allobs_df[glb_allobs_df$UniqueID == 11863, "D.P.air"] <- 0
# glb_allobs_df[(glb_allobs_df$D.P.air == 1) & (glb_allobs_df$productline == "Unknown"),
#               "prdline.my"] <- "iPad Air"

# print(glb_allobs_df[(glb_allobs_df$UniqueID %in% c(11767, 11811, 12156)),
#                     c(glb_id_var, "sold",
#     "prdline.my", "color", "condition", "cellular", "carrier", "storage"
#     #, "descr.my"
#     )])
# glb_allobs_df[glb_allobs_df$UniqueID == 11767, "prdline.my"] <- "iPad 2"
# glb_allobs_df[glb_allobs_df$UniqueID == 11767, "storage"] <- "32"
# glb_allobs_df[glb_allobs_df$UniqueID == 11811, "prdline.my"] <- "iPad 2"
# glb_allobs_df[glb_allobs_df$UniqueID == 12156, "prdline.my"] <- "iPad 1"

# mydsp_obs(list(prdline.my="Unknown"), all=TRUE)

# tmp_allobs_df <- glb_allobs_df[, "prdline.my", FALSE]
# names(tmp_allobs_df) <- "old.prdline.my"
# glb_allobs_df$prdline.my <-
#     plyr::revalue(glb_allobs_df$prdline.my, c(      
#         # "iPad 1"    = "iPad",
#         # "iPad 2"    = "iPad2+",
#         "iPad 3"    = "iPad 3+",
#         "iPad 4"    = "iPad 3+",
#         "iPad 5"    = "iPad 3+",
#         
#         "iPad Air"      = "iPadAir",
#         "iPad Air 2"    = "iPadAir",
#         
#         "iPad mini"         = "iPadmini",
#         "iPad mini 2"       = "iPadmini 2+",
#         "iPad mini 3"       = "iPadmini 2+",
#         "iPad mini Retina"  = "iPadmini 2+"
#     ))
# tmp_allobs_df$prdline.my <- glb_allobs_df[, "prdline.my"]
# print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my", "old.prdline.my")))
# print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my")))

# print(mycreate_sqlxtab_df(subset(glb_allobs_df, color == "Unknown"), 
#                         c("color", "D.P.black", "D.P.gold", "D.P.spacegray", "D.P.white")))
# print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.black > 0), 
#                     c(glb_id_var, "color", "D.P.black", "sold", "prdline.my", "condition",
#                       "cellular", "carrier", "storage", "descr.my")])
# glb_allobs_df[glb_allobs_df$UniqueID == 12137, "color"] <- "Black"

# print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.spacegray > 0),
#                     c(glb_id_var, "color", "D.P.spacegray", "prdline.my", "condition",
#                       "cellular", "carrier", "storage", "descr.my")])
# glb_allobs_df[glb_allobs_df$UniqueID %in% c(12106), "color"] <- "Space Gray"

# print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.white > 0),
#                     c(glb_id_var, "color", "D.P.white", "prdline.my", "condition",
#                       "cellular", "carrier", "storage", "descr.my")])
# glb_allobs_df[glb_allobs_df$UniqueID %in% c(10573, 10809, 10925, 11735), "color"] <-
#     "White"

glb_allobs_df$carrier.fctr <- as.factor(glb_allobs_df$carrier)
glb_allobs_df$cellular.fctr <- as.factor(glb_allobs_df$cellular)
glb_allobs_df$color.fctr <- as.factor(glb_allobs_df$color)
# glb_allobs_df$prdline.my.fctr <- as.factor(glb_allobs_df$prdline.my)
glb_allobs_df$storage.fctr <- as.factor(glb_allobs_df$storage)

#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
# glb_allobs_df %>% 
#     unite(prdl.descr.my, c(productline, as.numeric(D.chrs.n.log > 0), sep="#"))
#     unite_("prdl.descr.my", interp(~c("productline", as.numeric(D.chrs.n.log > 0), sep="#")))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))

# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))

#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
if (!is.null(glbFeatsPrice)) {
    for (var in glbFeatsPrice) {
        for (digit in 1:(log10(max(glb_allobs_df[, var], na.rm=TRUE)) + 1)) {
            glb_allobs_df[, paste0(var, ".dgt", digit, ".is9")] <- 
                as.numeric(as.integer((as.integer(glb_allobs_df[, var]) %% (10 ^ digit)) / 
                                          (10 ^ (digit - 1))) == 9)
#             glb_allobs_df[, paste0(var, ".dgt", digit, ".is9.fctr")] <- 
#                 as.factor(as.integer((as.integer(glb_allobs_df[, var]) %% (10 ^ digit)) / 
#                                           (10 ^ (digit - 1))) == 9)
        }
        for (decimal in 1:2) {
            glb_allobs_df[, paste0(var, ".dcm", decimal, ".is9")] <- 
                as.numeric(as.integer(glb_allobs_df[, var] * (10 ^ decimal)) %% 10 == 9)
#             glb_allobs_df[, paste0(var, ".dcm", decimal, ".is9.fctr")] <- 
#                 as.factor(as.integer(glb_allobs_df[, var] * (10 ^ decimal)) %% 10 == 9)
        }
    }
    #as.numeric((as.integer(startprice) %% 10) == 9)    
}

rm(corpus_lst
   , glb_sprs_DTM_lst #, glb_full_DTM_lst
   , txt_corpus, txt_vctr)
## Warning in rm(corpus_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr): object
## 'corpus_lst' not found
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end", 
                                     major.inc=TRUE)
##                        label step_major step_minor label_minor     bgn
## 9  extract.features_bind.DXM          8          0           0  80.382
## 10      extract.features_end          9          0           0 274.829
##        end elapsed
## 9  274.829 194.447
## 10      NA      NA
myplt_chunk(extract.features_chunk_df)
##                                                    label step_major
## 9                              extract.features_bind.DXM          8
## 5                          extract.features_build.corpus          4
## 7                            extract.features_report.DTM          6
## 8                              extract.features_bind.DTM          7
## 3                          extract.features_process.text          3
## 6                           extract.features_extract.DTM          5
## 2                    extract.features_factorize.str.vars          2
## 1                                   extract.features_bgn          1
## 4 extract.features_process.text_reporting_compound_terms          3
##   step_minor label_minor    bgn     end elapsed duration
## 9          0           0 80.382 274.829 194.447  194.447
## 5          0           0 48.741  71.464  22.723   22.723
## 7          0           0 72.831  77.561   4.731    4.730
## 8          0           0 77.562  80.382   2.820    2.820
## 3          0           0 47.145  48.734   1.589    1.589
## 6          0           0 71.465  72.831   1.366    1.366
## 2          0           0 47.074  47.144   0.071    0.070
## 1          0           0 47.057  47.074   0.017    0.017
## 4          1           1 48.735  48.740   0.005    0.005
## [1] "Total Elapsed Time: 274.829 secs"

# if (glb_save_envir)
#     save(glb_feats_df, 
#          glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
#          file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))

replay.petrisim(pn=glb_analytics_pn, 
    replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "data.training.all","data.new")), flip_coord=TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0

#glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=TRUE)

Step 3.0: extract features

glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)
##                 label step_major step_minor label_minor     bgn     end
## 5    extract.features          3          0           0  47.050 280.281
## 6 manage.missing.data          3          1           1 280.282      NA
##   elapsed
## 5 233.231
## 6      NA
# If mice crashes with error: Error in get(as.character(FUN), mode = "function", envir = envir) : object 'State' of mode 'function' was not found
#   consider excluding 'State' as a feature

# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0

mycheck_problem_data(glb_allobs_df, featsExclude = glbFeatsExclude, 
                     fctrMaxUniqVals = glbFctrMaxUniqVals)
## [1] "numeric data missing in : "
##      sold sold.fctr 
##       798       798 
## [1] "numeric data w/ 0s in : "
##                  biddable                      sold 
##                      1437                       995 
##              sprice.log10             cellular.fctr 
##                        31                      1590 
##       D.terms.post.stop.n   D.terms.post.stop.n.log 
##                      1516                      1516 
##    D.weight.post.stop.sum       D.terms.post.stem.n 
##                      1516                      1516 
##   D.terms.post.stem.n.log    D.weight.post.stem.sum 
##                      1516                      1516 
##                D.T.condit                   D.T.use 
##                      2156                      2363 
##                   D.T.in.                  D.T.good 
##                      2216                      2451 
##                D.T.screen                  D.T.with 
##                      2440                      2434 
##                    D.T.of                  D.T.mint 
##                      2497                      2585 
##                    D.T.or                D.T.cosmet 
##                      2523                      2531 
##                 D.T.minor                 D.T.light 
##                      2531                      2567 
##                  D.T.X100                  D.T.from 
##                      2584                      2591 
##                  D.T.hous              D.wrds.n.log 
##                      2576                      1513 
##          D.wrds.unq.n.log              D.weight.sum 
##                      1516                      1516 
## D.ratio.weight.sum.wrds.n              D.chrs.n.log 
##                      1516                      1513 
##         D.chrs.uppr.n.log              D.dgts.n.log 
##                      1515                      2452 
##       D.chrs.pnct01.n.log       D.chrs.pnct02.n.log 
##                      2570                      2648 
##       D.chrs.pnct03.n.log       D.chrs.pnct04.n.log 
##                      2647                      2648 
##       D.chrs.pnct05.n.log       D.chrs.pnct06.n.log 
##                      2582                      2596 
##       D.chrs.pnct07.n.log       D.chrs.pnct08.n.log 
##                      2611                      2572 
##       D.chrs.pnct09.n.log       D.chrs.pnct10.n.log 
##                      2632                      2639 
##       D.chrs.pnct11.n.log       D.chrs.pnct12.n.log 
##                      2294                      2534 
##       D.chrs.pnct13.n.log       D.chrs.pnct14.n.log 
##                      1930                      2574 
##       D.chrs.pnct15.n.log       D.chrs.pnct16.n.log 
##                      2628                      2639 
##       D.chrs.pnct17.n.log       D.chrs.pnct18.n.log 
##                      2646                      2647 
##       D.chrs.pnct19.n.log       D.chrs.pnct20.n.log 
##                      2648                      2648 
##       D.chrs.pnct21.n.log       D.chrs.pnct22.n.log 
##                      2648                      2648 
##       D.chrs.pnct23.n.log       D.chrs.pnct24.n.log 
##                      2648                      2648 
##       D.chrs.pnct25.n.log       D.chrs.pnct26.n.log 
##                      2648                      2648 
##       D.chrs.pnct27.n.log       D.chrs.pnct28.n.log 
##                      2648                      2640 
##       D.chrs.pnct29.n.log       D.chrs.pnct30.n.log 
##                      2648                      2648 
##         D.wrds.stop.n.log                  D.P.http 
##                      1965                      2648 
##                  D.P.mini                   D.P.air 
##                      2615                      2627 
##                 D.P.black                 D.P.white 
##                      2631                      2638 
##                  D.P.gold             D.P.spacegray 
##                      2647                      2642 
##       startprice.dgt1.is9       startprice.dgt2.is9 
##                      1779                      2290 
##       startprice.dgt3.is9       startprice.dcm1.is9 
##                      2643                      1653 
##       startprice.dcm2.is9 
##                      1826 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description   condition    cellular     carrier       color     storage 
##        1513           0           0           0           0           0 
## productline      .grpid    descr.my 
##           0          NA        1513
# glb_allobs_df <- na.omit(glb_allobs_df)

# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
    
    require(mice)
    set.seed(glb_mice_complete.seed)
    inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), 
                                union(glbFeatsExclude, glb_rsp_var))]
    print("Summary before imputation: ")
    print(summary(inp_impent_df))
    out_impent_df <- complete(mice(inp_impent_df))
    print(summary(out_impent_df))
    
    ret_vars <- sapply(names(out_impent_df), 
                       function(col) ifelse(!identical(out_impent_df[, col],
                                                       inp_impent_df[, col]), 
                                            col, ""))
    ret_vars <- ret_vars[ret_vars != ""]
    
    # complete(mice()) changes attributes of factors even though values don't change
    for (col in ret_vars) {
        if (inherits(out_impent_df[, col], "factor")) {
            if (identical(as.numeric(out_impent_df[, col]), 
                          as.numeric(inp_impent_df[, col])))
                ret_vars <- setdiff(ret_vars, col)
        }
    }
    return(out_impent_df[, ret_vars])
}

if (glb_impute_na_data && 
    (length(myfind_numerics_missing(glb_allobs_df)) > 0) &&
    (ncol(nonna_df <- glb_impute_missing_data()) > 0)) {
    for (col in names(nonna_df)) {
        glb_allobs_df[, paste0(col, ".nonNA")] <- nonna_df[, col]
        glbFeatsExclude <- c(glbFeatsExclude, col)        
    }
}    
    
mycheck_problem_data(glb_allobs_df, featsExclude = glbFeatsExclude, 
                     fctrMaxUniqVals = glbFctrMaxUniqVals, terminate = TRUE)
## [1] "numeric data missing in : "
##      sold sold.fctr 
##       798       798 
## [1] "numeric data w/ 0s in : "
##                  biddable                      sold 
##                      1437                       995 
##              sprice.log10             cellular.fctr 
##                        31                      1590 
##       D.terms.post.stop.n   D.terms.post.stop.n.log 
##                      1516                      1516 
##    D.weight.post.stop.sum       D.terms.post.stem.n 
##                      1516                      1516 
##   D.terms.post.stem.n.log    D.weight.post.stem.sum 
##                      1516                      1516 
##                D.T.condit                   D.T.use 
##                      2156                      2363 
##                   D.T.in.                  D.T.good 
##                      2216                      2451 
##                D.T.screen                  D.T.with 
##                      2440                      2434 
##                    D.T.of                  D.T.mint 
##                      2497                      2585 
##                    D.T.or                D.T.cosmet 
##                      2523                      2531 
##                 D.T.minor                 D.T.light 
##                      2531                      2567 
##                  D.T.X100                  D.T.from 
##                      2584                      2591 
##                  D.T.hous              D.wrds.n.log 
##                      2576                      1513 
##          D.wrds.unq.n.log              D.weight.sum 
##                      1516                      1516 
## D.ratio.weight.sum.wrds.n              D.chrs.n.log 
##                      1516                      1513 
##         D.chrs.uppr.n.log              D.dgts.n.log 
##                      1515                      2452 
##       D.chrs.pnct01.n.log       D.chrs.pnct02.n.log 
##                      2570                      2648 
##       D.chrs.pnct03.n.log       D.chrs.pnct04.n.log 
##                      2647                      2648 
##       D.chrs.pnct05.n.log       D.chrs.pnct06.n.log 
##                      2582                      2596 
##       D.chrs.pnct07.n.log       D.chrs.pnct08.n.log 
##                      2611                      2572 
##       D.chrs.pnct09.n.log       D.chrs.pnct10.n.log 
##                      2632                      2639 
##       D.chrs.pnct11.n.log       D.chrs.pnct12.n.log 
##                      2294                      2534 
##       D.chrs.pnct13.n.log       D.chrs.pnct14.n.log 
##                      1930                      2574 
##       D.chrs.pnct15.n.log       D.chrs.pnct16.n.log 
##                      2628                      2639 
##       D.chrs.pnct17.n.log       D.chrs.pnct18.n.log 
##                      2646                      2647 
##       D.chrs.pnct19.n.log       D.chrs.pnct20.n.log 
##                      2648                      2648 
##       D.chrs.pnct21.n.log       D.chrs.pnct22.n.log 
##                      2648                      2648 
##       D.chrs.pnct23.n.log       D.chrs.pnct24.n.log 
##                      2648                      2648 
##       D.chrs.pnct25.n.log       D.chrs.pnct26.n.log 
##                      2648                      2648 
##       D.chrs.pnct27.n.log       D.chrs.pnct28.n.log 
##                      2648                      2640 
##       D.chrs.pnct29.n.log       D.chrs.pnct30.n.log 
##                      2648                      2648 
##         D.wrds.stop.n.log                  D.P.http 
##                      1965                      2648 
##                  D.P.mini                   D.P.air 
##                      2615                      2627 
##                 D.P.black                 D.P.white 
##                      2631                      2638 
##                  D.P.gold             D.P.spacegray 
##                      2647                      2642 
##       startprice.dgt1.is9       startprice.dgt2.is9 
##                      1779                      2290 
##       startprice.dgt3.is9       startprice.dcm1.is9 
##                      2643                      1653 
##       startprice.dcm2.is9 
##                      1826 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description   condition    cellular     carrier       color     storage 
##        1513           0           0           0           0           0 
## productline      .grpid    descr.my 
##           0          NA        1513

Step 3.1: manage missing data

glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=FALSE)
##                 label step_major step_minor label_minor     bgn     end
## 6 manage.missing.data          3          1           1 280.282 280.448
## 7        cluster.data          3          2           2 280.448      NA
##   elapsed
## 6   0.166
## 7      NA
mycompute_entropy_df <- function(obs_df, entropy_var, by_var=NULL) {   
    require(lazyeval)
    require(dplyr)
    require(tidyr)

    if (is.null(by_var)) {
        by_var <- ".default"
        obs_df$.default <- as.factor(".default") 
    }
    
    if (!any(grepl(".clusterid", names(obs_df), fixed=TRUE)))
        obs_df$.clusterid <- 1
        
    cluster_df <- obs_df %>%
            count_(c(by_var, ".clusterid", entropy_var)) %>%
            dplyr::filter(n > 0) %>%
            dplyr::filter_(interp(~(!is.na(var)), var=as.name(entropy_var))) %>%
            unite_(paste0(by_var, ".clusterid"),
                   c(interp(by_var), ".clusterid")) %>%
            spread_(interp(entropy_var), "n", fill=0) 

#     head(cluster_df)
#     sum(cluster_df$n)
    tmp.entropy <- sapply(1:nrow(cluster_df),
            function(row) entropy(as.numeric(cluster_df[row, -1]), method = "ML"))
    tmp.knt <- sapply(1:nrow(cluster_df),
                    function(row) sum(as.numeric(cluster_df[row, -1])))
    cluster_df$.entropy <- tmp.entropy; cluster_df$.knt <- tmp.knt
    #print(cluster_df)
    return(cluster_df)
}
    
if (glb_cluster) {
    require(proxy)
    #require(hash)
    require(dynamicTreeCut)
    require(entropy)
    require(tidyr)
    require(ggdendro)

    mywgtdcosine_dist <- function(x, y=NULL, weights=NULL) {
        if (!inherits(x, "matrix"))
            x <- as.matrix(x)
    
        if (is.null(weights))
            weights <- rep(1, ncol(x))
    
        wgtsx <- matrix(rep(weights / sum(weights), nrow(x)), nrow = nrow(x),
                        byrow = TRUE)
        wgtdx <- x * wgtsx
    
        wgtdxsqsum <- as.matrix(rowSums((x ^ 2) * wgtsx), byrow=FALSE)
        denom <- sqrt(wgtdxsqsum %*% t(wgtdxsqsum))
    
        ret_mtrx <- 1 - ((sum(weights) ^ 1) * (wgtdx %*% t(wgtdx)) / denom)
        ret_mtrx[is.nan(ret_mtrx)] <- 1
        diag(ret_mtrx) <- 0
        return(ret_mtrx)
    }
    #pr_DB$delete_entry("mywgtdcosine"); 
    # Need to do this only once across runs ?
    if (!pr_DB$entry_exists("mywgtdcosine")) {
        pr_DB$set_entry(FUN = mywgtdcosine_dist, names = c("mywgtdcosine"))
        pr_DB$modify_entry(names="mywgtdcosine", type="metric", loop=FALSE)
    }
    #pr_DB$get_entry("mywgtdcosine")

#     glb_hash <- hash(key=unique(glb_allobs_df$myCategory), 
#                      values=1:length(unique(glb_allobs_df$myCategory)))
#     glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory), 
#                      values=1:length(unique(glb_allobs_df$myCategory)))
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
    cluster_vars <- grep(paste0("[", 
                        toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
                                      "]\\.[PT]\\."), 
                               names(glb_allobs_df), value = TRUE)
    # Assign correlations with rsp_var as weights for cosine distance
    print("Clustering features: ")
    cluster_vars_df <- data.frame(abs.cor.y = abs(cor(
                        glb_allobs_df[glb_allobs_df$.src == "Train", cluster_vars],
            as.numeric(glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var]),
                                    use = "pairwise.complete.obs")))
    print(tail(cluster_vars_df <- orderBy(~ abs.cor.y, 
                                    subset(cluster_vars_df, !is.na(abs.cor.y))), 5))
    print(sprintf("    .rnorm cor: %0.4f",
        cor(glb_allobs_df[glb_allobs_df$.src == "Train", ".rnorm"], 
            as.numeric(glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var]),
            use = "pairwise.complete.obs")))
    
    print(sprintf("glb_allobs_df Entropy: %0.4f", 
        allobs_ent <- entropy(table(glb_allobs_df[, glb_cluster_entropy_var]),
                              method="ML")))
    
    print(category_df <- mycompute_entropy_df(obs_df=glb_allobs_df,
                                             entropy_var=glb_cluster_entropy_var,
                                             by_var=glb_category_var))
    print(sprintf("glb_allobs_df$%s Entropy: %0.4f (%0.4f pct)",
                    glb_category_var,
            category_ent <- weighted.mean(category_df$.entropy, category_df$.knt),
                    100 * category_ent / allobs_ent))

    glb_allobs_df$.clusterid <- 1    
    #print(max(table(glb_allobs_df$myCategory.fctr) / 20))
    
#stop(here"); glb_to_sav()    
    grp_ids <- sort(unique(glb_allobs_df[, glb_category_var]))
    glb_cluster_size_df_lst <- list()
    png(paste0(glb_out_pfx, "FeatsTxtClusters.png"), 
        width = 480 * 2, height = 480 * length(grp_ids))
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(nrow = length(grp_ids), ncol = 2)))
    pltIx <- 1
    for (grp in grp_ids) {
# if (grep(grp, levels(grp_ids)) <= 6) next                
# if (grep(grp, levels(grp_ids)) > 9) next        
# if (grep(grp, levels(grp_ids)) != 10) next        
        print(sprintf("Category: %s", grp))
        ctgry_allobs_df <- glb_allobs_df[glb_allobs_df[, glb_category_var] == grp, ]
        if (!inherits(ctgry_allobs_df[, glb_cluster_entropy_var], "factor"))
            ctgry_allobs_df[, glb_cluster_entropy_var] <- 
                as.factor(ctgry_allobs_df[, glb_cluster_entropy_var])
        
        #dstns_dist <- proxy::dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
        dstns_dist <- proxy::dist(ctgry_allobs_df[, row.names(cluster_vars_df)], 
                                  method = "mywgtdcosine",
                                  weights = cluster_vars_df$abs.cor.y)
        # Custom distance functions return a crossdist object
        #dstns_mtrx <- as.matrix(dstns_dist)
        dstns_mtrx <- matrix(as.vector(dstns_dist), nrow=attr(dstns_dist, "dim")[1],
                             dimnames=attr(dstns_dist, "dimnames"))
        dstns_dist <- as.dist(dstns_mtrx)

        print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
#         print(dim(dstns_mtrx))        
#         print(sprintf("which.max: %d", which.max(dstns_mtrx)))
        row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
        col_ix <- which.max(dstns_mtrx[row_ix, ])
#         print(sprintf("row_ix: %d", row_ix)); print(sprintf("col_ix: %d", col_ix));
#         print(dim(ctgry_allobs_df))
        print(ctgry_allobs_df[c(row_ix, col_ix), 
            c(glb_id_var, glb_cluster_entropy_var, glb_category_var, glbFeatsText, cluster_vars)])
    
        min_dstns_mtrx <- dstns_mtrx
        diag(min_dstns_mtrx) <- 1
        # Float representations issue -2.22e-16 vs. 0.0000
        print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
        row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
        col_ix <- which.min(min_dstns_mtrx[row_ix, ])
        print(ctgry_allobs_df[c(row_ix, col_ix), 
            c(glb_id_var, glb_cluster_entropy_var, glb_category_var, glbFeatsText,
              cluster_vars)])
    
        set.seed(glb_cluster.seed)
        clusters <- hclust(dstns_dist, method = "ward.D2")
        # Workaround to avoid "Error in cutree(dendro, h = heightcutoff) : the 'height' component of 'tree' is not sorted (increasingly)"
        if (with(clusters,all.equal(height,sort(height))))
            clusters$height <- round(clusters$height,6)
        
        clusters$labels <- ctgry_allobs_df[, glb_id_var]
        clustersDD <- dendro_data(clusters)
        clustersDD$labels[, glb_rsp_var] <- sapply(clustersDD$labels$label, function(id)
            ctgry_allobs_df[id == ctgry_allobs_df[, glb_id_var], glb_rsp_var])
        print(ggdendrogram(clustersDD, rotate = TRUE, size = 2) + 
                geom_point(data = clustersDD$labels, 
            aes_string(x = "x", color = glb_rsp_var), y = min(clustersDD$segments$y)) + 
                coord_flip(ylim = c(min(clustersDD$segments$y),
                                         max(clustersDD$segments$y))) + 
                ggtitle(grp),
            vp = viewport(layout.pos.row = pltIx, layout.pos.col = 1))  
        
#         clusters$labels <- ctgry_allobs_df[, glb_id_var]
#         clustersDD <- dendro_data(clusters)
#         clustersDD$labels$color <- sapply(clustersDD$labels$label, function(id)
#             ctgry_allobs_df[id == ctgry_allobs_df[, glb_id_var], glb_rsp_var])
#         print(ggdendrogram(clustersDD, rotate = TRUE, size = 2) + 
#                 geom_point(data = clustersDD$labels, 
#                 aes_string(x = "x", color = "color"), y = min(clustersDD$segments$y)) + 
#                 coord_flip(ylim = c(min(clustersDD$segments$y),
#                                          max(clustersDD$segments$y))))
#         print(ggdendrogram(clustersDD, rotate = TRUE, size = 2) + 
#                 geom_point(data = clustersDD$labels, 
#                           aes_string(x = "x", y = "y", color = "color")))
#         myplclust(clusters, lab=ctgry_allobs_df[, glb_id_var], 
#                   lab.col=unclass(ctgry_allobs_df[, glb_cluster_entropy_var]))

        opt_minclustersize_df <- data.frame(minclustersize = nrow(ctgry_allobs_df), 
            entropy = entropy(table(ctgry_allobs_df[, glb_cluster_entropy_var]),
                              method = "ML"))
        for (minclustersize in 
             as.integer(seq(nrow(ctgry_allobs_df) / 2, nrow(ctgry_allobs_df) / 10, 
                            length = 5))) {
            clusterGroups <- cutreeDynamic(clusters, minClusterSize = minclustersize,
                                           method = "tree", deepSplit = 0)
            # Unassigned groups are labeled 0; the largest group has label 1
            clusterGroups[clusterGroups == 0] <- 1
            ctgry_allobs_df$.clusterid <- clusterGroups
            ctgry_clstrs_df <- mycompute_entropy_df(ctgry_allobs_df,
                                                    glb_cluster_entropy_var)
            opt_minclustersize_df <- rbind(opt_minclustersize_df, 
                                           data.frame(minclustersize = minclustersize,
                entropy = weighted.mean(ctgry_clstrs_df$.entropy, ctgry_clstrs_df$.knt)))
        }
        opt_minclustersize <-
            opt_minclustersize_df$minclustersize[which.min(opt_minclustersize_df$entropy)]
        opt_minclustersize_df$.color <- 
            ifelse(opt_minclustersize_df$minclustersize == opt_minclustersize,
                   "red", "blue")
        print(ggplot(data = opt_minclustersize_df, 
                     mapping = aes(x = minclustersize, y = entropy)) + 
                geom_point(aes(color = .color)) + scale_color_identity() + 
                guides(color = "none") + geom_line(),
            vp = viewport(layout.pos.row = pltIx, layout.pos.col = 2))
        glb_cluster_size_df_lst[[grp]] <- opt_minclustersize_df
        
        # select minclustersize that minimizes entropy
        clusterGroups <- cutreeDynamic(clusters, minClusterSize = opt_minclustersize,
                                       method = "tree",
                                       deepSplit = 0)
        # Unassigned groups are labeled 0; the largest group has label 1
        table(clusterGroups, ctgry_allobs_df[, glb_cluster_entropy_var], 
              useNA = "ifany")   
        clusterGroups[clusterGroups == 0] <- 1
        table(clusterGroups, ctgry_allobs_df[, glb_cluster_entropy_var], useNA = "ifany") 
        glb_allobs_df[glb_allobs_df[, glb_category_var] == grp,]$.clusterid <-
            clusterGroups
        
        pltIx <- pltIx + 1
    }
    dev.off()
    #all.equal(sav_allobs_df_clusterid, glb_allobs_df$.clusterid)
    
    print(cluster_df <- mycompute_entropy_df(obs_df=glb_allobs_df,
                                             entropy_var=glb_cluster_entropy_var,
                                             by_var=glb_category_var))
    print(sprintf("glb_allobs_df$%s$.clusterid Entropy: %0.4f (%0.4f pct)",
                    glb_category_var,
                cluster_ent <- weighted.mean(cluster_df$.entropy, cluster_df$.knt),
                    100 * cluster_ent / category_ent))

    glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
    # .clusterid.fctr is created automatically (probably ?) later
    glbFeatsExclude <- c(glbFeatsExclude, ".clusterid")
    if (!is.null(glb_category_var))
#         glb_interaction_only_feats_lst[ifelse(grepl("\\.fctr", glb_category_var),
#                                             glb_category_var, 
#                                             paste0(glb_category_var, ".fctr"))] <-
#             c(".clusterid.fctr")
        glb_interaction_only_feats_lst[[".clusterid.fctr"]] <-
            ifelse(grepl("\\.fctr", glb_category_var), glb_category_var, 
                                                        paste0(glb_category_var, ".fctr"))
            
    if (glbFeatsTextClusterVarsExclude)
        glbFeatsExclude <- c(glbFeatsExclude, cluster_vars)
}
## Loading required package: proxy
## 
## Attaching package: 'proxy'
## 
## The following objects are masked from 'package:stats':
## 
##     as.dist, dist
## 
## The following object is masked from 'package:base':
## 
##     as.matrix
## 
## Loading required package: dynamicTreeCut
## Loading required package: entropy
## Loading required package: ggdendro
## [1] "Clustering features: "
## Warning in cor(glb_allobs_df[glb_allobs_df$.src == "Train",
## cluster_vars], : the standard deviation is zero
##             abs.cor.y
## D.T.with   0.06608760
## D.T.in.    0.07165392
## D.T.cosmet 0.08848086
## D.T.X100   0.11139850
## D.T.hous   0.12927488
## [1] "    .rnorm cor: -0.0013"
## [1] "glb_allobs_df Entropy: 0.6903"
## Loading required package: lazyeval
## Source: local data frame [10 x 5]
## 
##    prdl.my.fctr.clusterid     N     Y  .entropy  .knt
##                     (chr) (dbl) (dbl)     (dbl) (dbl)
## 1               Unknown_1   122    82 0.6737987   204
## 2                 iPad1_1   100   125 0.6869616   225
## 3                 iPad2_1   139   147 0.6927559   286
## 4                 iPad3_1    73    80 0.6921002   153
## 5                 iPad4_1    93    64 0.6759893   157
## 6              iPadAir2_1   100    71 0.6786969   171
## 7               iPadAir_1   102    78 0.6842318   180
## 8             iPadmini2_1    58    49 0.6896056   107
## 9             iPadmini3_1    63    27 0.6108643    90
## 10             iPadmini_1   145   132 0.6920455   277
## [1] "glb_allobs_df$prdl.my.fctr Entropy: 0.6821 (98.8123 pct)"
## [1] "Category: Unknown"
## [1] "max distance(1.0000) pair:"
##    UniqueID sold.fctr prdl.my.fctr
## 5     10005         N      Unknown
## 24    10024         N      Unknown
##                                                                                                descr.my
## 5  Please feel free to buy. All products have been thoroughly inspected, cleaned and tested to be 100% 
## 24                                                                                                     
##    D.T.condit D.T.use D.T.in. D.T.good D.T.screen D.T.with D.T.of D.T.mint
## 5           0       0       0        0          0        0      0        0
## 24          0       0       0        0          0        0      0        0
##    D.T.or D.T.cosmet D.T.minor D.T.light  D.T.X100 D.T.from D.T.hous
## 5       0          0         0         0 0.4475573        0        0
## 24      0          0         0         0 0.0000000        0        0
##    D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 5         0        0       0         0         0        0             0
## 24        0        0       0         0         0        0             0
## [1] "min distance(0.8891) pair:"
##     UniqueID sold.fctr prdl.my.fctr
## 5      10005         N      Unknown
## 419    10420         Y      Unknown
##                                                                                                  descr.my
## 5    Please feel free to buy. All products have been thoroughly inspected, cleaned and tested to be 100% 
## 419 THIS ITEM WAS A STORE DEMO BUT HAS 100% BEEN RESTORED TO FACTORY SETTINGS AND IS READY FOR USE. DOES 
##     D.T.condit   D.T.use D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 5            0 0.0000000       0        0          0        0      0
## 419          0 0.1891688       0        0          0        0      0
##     D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light  D.T.X100 D.T.from
## 5          0      0          0         0         0 0.4475573        0
## 419        0      0          0         0         0 0.3159228        0
##     D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 5          0        0        0       0         0         0        0
## 419        0        0        0       0         0         0        0
##     D.P.spacegray
## 5               0
## 419             0
## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf
## [1] "Category: iPad1"
## [1] "max distance(1.0000) pair:"
##    UniqueID sold.fctr prdl.my.fctr descr.my D.T.condit D.T.use D.T.in.
## 9     10009         Y        iPad1                   0       0       0
## 12    10012         N        iPad1                   0       0       0
##    D.T.good D.T.screen D.T.with D.T.of D.T.mint D.T.or D.T.cosmet
## 9         0          0        0      0        0      0          0
## 12        0          0        0      0        0      0          0
##    D.T.minor D.T.light D.T.X100 D.T.from D.T.hous D.P.http D.P.mini
## 9          0         0        0        0        0        0        0
## 12         0         0        0        0        0        0        0
##    D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 9        0         0         0        0             0
## 12       0         0         0        0             0
## [1] "min distance(0.8976) pair:"
##      UniqueID sold.fctr prdl.my.fctr
## 2290    12301      <NA>        iPad1
## 2326    12337      <NA>        iPad1
##                                                                                                 descr.my
## 2290 A device listed in Average/Fair  condition with some scratches, scuffs on the housing & screen (do 
## 2326 A device listed in Average/Fair  condition with some scratches, scuffs on the housing & screen (do 
##      D.T.condit D.T.use   D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 2290  0.1618782       0 0.1743867        0  0.2446832 0.241948      0
## 2326  0.1618782       0 0.1743867        0  0.2446832 0.241948      0
##      D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light D.T.X100 D.T.from
## 2290        0      0          0         0         0        0        0
## 2326        0      0          0         0         0        0        0
##       D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 2290 0.3467175        0        0       0         0         0        0
## 2326 0.3467175        0        0       0         0         0        0
##      D.P.spacegray
## 2290             0
## 2326             0
## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf
## [1] "Category: iPad2"
## [1] "max distance(1.0000) pair:"
##    UniqueID sold.fctr prdl.my.fctr
## 1     10001         N        iPad2
## 21    10021         Y        iPad2
##                                             descr.my D.T.condit D.T.use
## 1  iPad is in 8point5+ out of 10 cosmetic condition!  0.3035216       0
## 21                                  Crack on screen.  0.0000000       0
##     D.T.in. D.T.good D.T.screen D.T.with    D.T.of D.T.mint D.T.or
## 1  0.326975        0   0.000000        0 0.5165353        0      0
## 21 0.000000        0   1.223416        0 0.0000000        0      0
##    D.T.cosmet D.T.minor D.T.light D.T.X100 D.T.from D.T.hous D.P.http
## 1   0.5625403         0         0        0        0        0        0
## 21  0.0000000         0         0        0        0        0        0
##    D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 1         0       0         0         0        0             0
## 21        0       0         0         0        0             0
## [1] "min distance(0.8886) pair:"
##     UniqueID sold.fctr prdl.my.fctr
## 431    10432         N        iPad2
## 664    10665         N        iPad2
##                                                                                                 descr.my
## 431  *FREE* Same-Day Ship | Factory Refurbished | 90-Day Warranty | 100% Functional, Includes All Major 
## 664 100% Fully Functional, Clean ESN & iCloud Clear, professionally restored, inspected, & tested. This 
##     D.T.condit D.T.use D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 431          0       0       0        0          0        0      0
## 664          0       0       0        0          0        0      0
##     D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light  D.T.X100 D.T.from
## 431        0      0          0         0         0 0.4882443        0
## 664        0      0          0         0         0 0.4882443        0
##     D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 431        0        0        0       0         0         0        0
## 664        0        0        0       0         0         0        0
##     D.P.spacegray
## 431             0
## 664             0
## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf
## [1] "No module detected"
## [1] "Category: iPad3"
## [1] "max distance(1.0000) pair:"
##    UniqueID sold.fctr prdl.my.fctr
## 17    10017         Y        iPad3
## 37    10037         Y        iPad3
##                                                                                                descr.my
## 17 Great working iPad.  Very minor surface scratches on back as pictured.  Other very light scratching 
## 37                                                                                 Rarely ever used it.
##    D.T.condit  D.T.use D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 17          0 0.000000       0        0          0        0      0
## 37          0 1.607935       0        0          0        0      0
##    D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light D.T.X100 D.T.from
## 17        0      0          0 0.3214516 0.3593455        0        0
## 37        0      0          0 0.0000000 0.0000000        0        0
##    D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 17        0        0        0       0         0         0        0
## 37        0        0        0       0         0         0        0
##    D.P.spacegray
## 17             0
## 37             0
## [1] "min distance(0.8886) pair:"
##      UniqueID sold.fctr prdl.my.fctr
## 946     10949         N        iPad3
## 1910    11921      <NA>        iPad3
##                                                                                              descr.my
## 946  *FREE* Same-Day Ship | 90-Day Warranty | 100% Functional, Includes All Major Accessories, Shows 
## 1910 *FREE* Same-Day Ship | 90-Day Warranty | 100% Functional, Includes All Major Accessories, Shows 
##      D.T.condit D.T.use D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 946           0       0       0        0          0        0      0
## 1910          0       0       0        0          0        0      0
##      D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light  D.T.X100 D.T.from
## 946         0      0          0         0         0 0.4882443        0
## 1910        0      0          0         0         0 0.4882443        0
##      D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 946         0        0        0       0         0         0        0
## 1910        0        0        0       0         0         0        0
##      D.P.spacegray
## 946              0
## 1910             0
## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf
## [1] "Category: iPad4"
## [1] "max distance(1.0000) pair:"
##    UniqueID sold.fctr prdl.my.fctr descr.my D.T.condit D.T.use D.T.in.
## 3     10003         Y        iPad4                   0       0       0
## 10    10010         Y        iPad4                   0       0       0
##    D.T.good D.T.screen D.T.with D.T.of D.T.mint D.T.or D.T.cosmet
## 3         0          0        0      0        0      0          0
## 10        0          0        0      0        0      0          0
##    D.T.minor D.T.light D.T.X100 D.T.from D.T.hous D.P.http D.P.mini
## 3          0         0        0        0        0        0        0
## 10         0         0        0        0        0        0        0
##    D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 3        0         0         0        0             0
## 10       0         0         0        0             0
## [1] "min distance(0.8886) pair:"
##      UniqueID sold.fctr prdl.my.fctr
## 63      10063         N        iPad4
## 2308    12319      <NA>        iPad4
##                                                                                              descr.my
## 63   *FREE* Same-Day Ship | 90-Day Warranty | 100% Functional, Includes All Major Accessories, Shows 
## 2308 *FREE* Same-Day Ship | 90-Day Warranty | 100% Functional, Includes All Major Accessories, Shows 
##      D.T.condit D.T.use D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 63            0       0       0        0          0        0      0
## 2308          0       0       0        0          0        0      0
##      D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light  D.T.X100 D.T.from
## 63          0      0          0         0         0 0.4882443        0
## 2308        0      0          0         0         0 0.4882443        0
##      D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 63          0        0        0       0         0         0        0
## 2308        0        0        0       0         0         0        0
##      D.P.spacegray
## 63               0
## 2308             0
## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf
## [1] "Category: iPadAir"
## [1] "max distance(1.0000) pair:"
##    UniqueID sold.fctr prdl.my.fctr descr.my D.T.condit D.T.use D.T.in.
## 16    10016         N      iPadAir                   0       0       0
## 25    10025         N      iPadAir                   0       0       0
##    D.T.good D.T.screen D.T.with D.T.of D.T.mint D.T.or D.T.cosmet
## 16        0          0        0      0        0      0          0
## 25        0          0        0      0        0      0          0
##    D.T.minor D.T.light D.T.X100 D.T.from D.T.hous D.P.http D.P.mini
## 16         0         0        0        0        0        0        0
## 25         0         0        0        0        0        0        0
##    D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 16       0         0         0        0             0
## 25       0         0         0        0             0
## [1] "min distance(0.8886) pair:"
##      UniqueID sold.fctr prdl.my.fctr
## 167     10167         N      iPadAir
## 1156    11162         N      iPadAir
##                                                                                              descr.my
## 167                                                           LIKE BRANDNEW 100% MONEY BACK GUARANTEE
## 1156 *FREE* Same-Day Ship | 90-Day Warranty | 100% Functional, Includes All Major Accessories, Shows 
##      D.T.condit D.T.use D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 167           0       0       0        0          0        0      0
## 1156          0       0       0        0          0        0      0
##      D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light  D.T.X100 D.T.from
## 167         0      0          0         0         0 1.7902291        0
## 1156        0      0          0         0         0 0.4882443        0
##      D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 167         0        0        0       0         0         0        0
## 1156        0        0        0       0         0         0        0
##      D.P.spacegray
## 167              0
## 1156             0
## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf
## [1] "Category: iPadAir2"
## [1] "max distance(1.0000) pair:"
##    UniqueID sold.fctr prdl.my.fctr descr.my D.T.condit D.T.use D.T.in.
## 19    10019         Y     iPadAir2                   0       0       0
## 35    10035         N     iPadAir2                   0       0       0
##    D.T.good D.T.screen D.T.with D.T.of D.T.mint D.T.or D.T.cosmet
## 19        0          0        0      0        0      0          0
## 35        0          0        0      0        0      0          0
##    D.T.minor D.T.light D.T.X100 D.T.from D.T.hous D.P.http D.P.mini
## 19         0         0        0        0        0        0        0
## 35         0         0        0        0        0        0        0
##    D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 19       0         0         0        0             0
## 35       0         0         0        0             0
## [1] "min distance(0.9283) pair:"
##      UniqueID sold.fctr prdl.my.fctr
## 1214    11220         N     iPadAir2
## 1856    11867      <NA>     iPadAir2
##                                                                                               descr.my
## 1214 Brandnew, iPad untouched and still wrapped in original plastic inside box. Opened only to verify 
## 1856           Brandnew factory sealed iPad in an OPEN BOX...THE BOX ITSELF IS HEAVILY DISTRESSED(see 
##      D.T.condit D.T.use   D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 1214          0       0 0.2378000        0          0        0      0
## 1856          0       0 0.2012154        0          0        0      0
##      D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light D.T.X100 D.T.from
## 1214        0      0          0         0         0        0        0
## 1856        0      0          0         0         0        0        0
##      D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 1214        0        0        0       0         0         0        0
## 1856        0        0        0       0         0         0        0
##      D.P.spacegray
## 1214             0
## 1856             0
## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf
## [1] "No module detected"
## [1] "Category: iPadmini"
## [1] "max distance(1.0000) pair:"
##    UniqueID sold.fctr prdl.my.fctr descr.my D.T.condit D.T.use D.T.in.
## 7     10007         Y     iPadmini                   0       0       0
## 57    10057         N     iPadmini                   0       0       0
##    D.T.good D.T.screen D.T.with D.T.of D.T.mint D.T.or D.T.cosmet
## 7         0          0        0      0        0      0          0
## 57        0          0        0      0        0      0          0
##    D.T.minor D.T.light D.T.X100 D.T.from D.T.hous D.P.http D.P.mini
## 7          0         0        0        0        0        0        0
## 57         0         0        0        0        0        0        0
##    D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 7        0         0         0        0             0
## 57       0         0         0        0             0
## [1] "min distance(0.8919) pair:"
##      UniqueID sold.fctr prdl.my.fctr
## 93      10093         N     iPadmini
## 2364    12375      <NA>     iPadmini
##                                                                                               descr.my
## 93    *FREE* Same-Day Ship | 90-Day Warranty | 100% Functional, Includes All Major Accessories, Shows 
## 2364 100% working condition, No icloud blocks, tablet is ready for new user. See item description for 
##      D.T.condit D.T.use D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 93    0.0000000       0       0        0          0        0      0
## 2364  0.1734409       0       0        0          0        0      0
##      D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light  D.T.X100 D.T.from
## 93          0      0          0         0         0 0.4882443        0
## 2364        0      0          0         0         0 0.3836205        0
##      D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 93          0        0        0       0         0         0        0
## 2364        0        0        0       0         0         0        0
##      D.P.spacegray
## 93               0
## 2364             0
## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf
## [1] "No module detected"
## [1] "Category: iPadmini2"
## [1] "max distance(1.0000) pair:"
##   UniqueID sold.fctr prdl.my.fctr descr.my D.T.condit D.T.use D.T.in.
## 4    10004         N    iPadmini2                   0       0       0
## 6    10006         Y    iPadmini2                   0       0       0
##   D.T.good D.T.screen D.T.with D.T.of D.T.mint D.T.or D.T.cosmet D.T.minor
## 4        0          0        0      0        0      0          0         0
## 6        0          0        0      0        0      0          0         0
##   D.T.light D.T.X100 D.T.from D.T.hous D.P.http D.P.mini D.P.air D.P.black
## 4         0        0        0        0        0        0       0         0
## 6         0        0        0        0        0        0       0         0
##   D.P.white D.P.gold D.P.spacegray
## 4         0        0             0
## 6         0        0             0
## [1] "min distance(0.9126) pair:"
##      UniqueID sold.fctr prdl.my.fctr
## 927     10930         N    iPadmini2
## 2020    12031      <NA>    iPadmini2
##                                                                                                       descr.my
## 927                   Flawless Condition(10/10) 100% functional with Flawless Retina Display. Unit is free of 
## 2020 Good Condition(8point25/10), 100% functional with Flawless Retina Display. Unit has a dent on upper left 
##      D.T.condit D.T.use D.T.in.  D.T.good D.T.screen  D.T.with    D.T.of
## 927   0.1867825       0       0 0.0000000          0 0.2791708 0.3178679
## 2020  0.1734409       0       0 0.2677597          0 0.2592300 0.0000000
##      D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light  D.T.X100 D.T.from
## 927         0      0          0         0         0 0.4131298        0
## 2020        0      0          0         0         0 0.3836205        0
##      D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 927         0        0        0       0         0         0        0
## 2020        0        0        0       0         0         0        0
##      D.P.spacegray
## 927              0
## 2020             0
## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf
## [1] "Category: iPadmini3"
## [1] "max distance(1.0000) pair:"
##     UniqueID sold.fctr prdl.my.fctr descr.my D.T.condit D.T.use D.T.in.
## 8      10008         N    iPadmini3                   0       0       0
## 120    10120         N    iPadmini3                   0       0       0
##     D.T.good D.T.screen D.T.with D.T.of D.T.mint D.T.or D.T.cosmet
## 8          0          0        0      0        0      0          0
## 120        0          0        0      0        0      0          0
##     D.T.minor D.T.light D.T.X100 D.T.from D.T.hous D.P.http D.P.mini
## 8           0         0        0        0        0        0        0
## 120         0         0        0        0        0        0        0
##     D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 8         0         0         0        0             0
## 120       0         0         0        0             0
## [1] "min distance(0.9488) pair:"
##      UniqueID sold.fctr prdl.my.fctr
## 1011    11014         N    iPadmini3
## 1115    11121         N    iPadmini3
##                                                                                                  descr.my
## 1011   Brandnew IpadMini, still in plastic wrap and box provided by hospital with cords and new unopened 
## 1115 Bought for one day and removed iPad plastic cover, never used. In original box with all accessories!
##      D.T.condit   D.T.use   D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 1011          0 0.0000000 0.2179833        0          0 0.302435      0
## 1115          0 0.2143913 0.1743867        0          0 0.241948      0
##      D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light D.T.X100 D.T.from
## 1011        0      0          0         0         0        0        0
## 1115        0      0          0         0         0        0        0
##      D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 1011        0        0        1       0         0         0        0
## 1115        0        0        0       0         0         0        0
##      D.P.spacegray
## 1011             0
## 1115             0
## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf
## [1] "No module detected"
## Source: local data frame [19 x 5]
## 
##    prdl.my.fctr.clusterid     N     Y  .entropy  .knt
##                     (chr) (dbl) (dbl)     (dbl) (dbl)
## 1               Unknown_1    92    62 0.6740508   154
## 2               Unknown_2    17    12 0.6782094    29
## 3               Unknown_3    13     8 0.6645284    21
## 4                 iPad1_1    72   104 0.6765260   176
## 5                 iPad1_2    15    12 0.6869616    27
## 6                 iPad1_3    13     9 0.6765260    22
## 7                 iPad2_1   139   147 0.6927559   286
## 8                 iPad3_1    60    75 0.6869616   135
## 9                 iPad3_2    13     5 0.5908422    18
## 10                iPad4_1    52    52 0.6931472   104
## 11                iPad4_2    16    12 0.6829081    28
## 12                iPad4_3    25     0 0.0000000    25
## 13             iPadAir2_1   100    71 0.6786969   171
## 14              iPadAir_1    86    60 0.6772057   146
## 15              iPadAir_2    16    18 0.6914161    34
## 16            iPadmini2_1    50    44 0.6911087    94
## 17            iPadmini2_2     8     5 0.6662784    13
## 18            iPadmini3_1    63    27 0.6108643    90
## 19             iPadmini_1   145   132 0.6920455   277
## [1] "glb_allobs_df$prdl.my.fctr$.clusterid Entropy: 0.6710 (98.3773 pct)"
# Last call for data modifications 
#stop(here") # sav_allobs_df <- glb_allobs_df
# glb_allobs_df[(glb_allobs_df$PropR == 0.75) & (glb_allobs_df$State == "Hawaii"), "PropR.fctr"] <- "N"

# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")

glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)
##                     label step_major step_minor label_minor     bgn
## 7            cluster.data          3          2           2 280.448
## 8 partition.data.training          4          0           0 330.238
##       end elapsed
## 7 330.237  49.789
## 8      NA      NA

Step 4.0: partition data training

#stop(here"); glb_to_sav()
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
    set.seed(glb_split_sample.seed)
    OOB_size <- nrow(glb_newobs_df) * 1.1
    
    if (is.null(glb_category_var)) {
        require(caTools)
        split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw], 
                              SplitRatio=OOB_size / nrow(glb_trnobs_df))
        glb_OOBobs_df <- glb_trnobs_df[split ,]            
        glb_fitobs_df <- glb_trnobs_df[!split, ] 
    } else {
        sample_vars <- c(glb_category_var, glb_rsp_var_raw)
        rspvar_freq_df <- orderBy(reformulate(glb_rsp_var_raw), 
                                 mycreate_sqlxtab_df(glb_trnobs_df, glb_rsp_var_raw))
        OOB_rspvar_size <- 
            1.0 * OOB_size * rspvar_freq_df$.n / sum(rspvar_freq_df$.n) 
        names(OOB_rspvar_size) <- as.character(rspvar_freq_df[, glb_rsp_var_raw])
        newobs_freq_df <- orderBy(reformulate(glb_category_var),
                                mycreate_sqlxtab_df(glb_newobs_df, glb_category_var))
        trnobs_freq_df <- orderBy(reformulate(glb_category_var),
                                mycreate_sqlxtab_df(glb_trnobs_df, glb_category_var))
        ctgry_freq_df <- merge(newobs_freq_df, trnobs_freq_df, 
                                by = glb_category_var,
                                all = TRUE, sort = TRUE, 
                                suffixes = c(".tst", ".trn"))
        ctgry_freq_df[is.na(ctgry_freq_df)] <- 0
        
        obs_freq_df <- mycreate_xtab_df(glb_trnobs_df, 
                                        c(glb_category_var, glb_rsp_var_raw))
        newobs_freq_df <- orderBy(reformulate(glb_category_var),
                                mycreate_sqlxtab_df(glb_newobs_df, glb_category_var))
        names(newobs_freq_df) <- gsub(".n", ".n.tst", names(newobs_freq_df), 
                                      fixed = TRUE)
        obs_freq_df <- merge(obs_freq_df, newobs_freq_df, all = TRUE)
        strata_mtrx <- ceiling(
            matrix(obs_freq_df$.n.tst * 1.0 / sum(obs_freq_df$.n.tst)) %*%
                      matrix(OOB_rspvar_size, nrow = 1))
        dimnames(strata_mtrx)[[1]] <- obs_freq_df[, glb_category_var]
        dimnames(strata_mtrx)[[2]] <- as.character(rspvar_freq_df[, glb_rsp_var_raw])
        for (val in rspvar_freq_df[, glb_rsp_var_raw]) {
            trn <- paste0(glb_rsp_var_raw, ".", as.character(val))
            strata <- paste0(".strata.", as.character(val))            
            obs_freq_df[, strata] <- strata_mtrx[, as.character(val)]
            if (length((ix <- which(obs_freq_df[, trn] < 
                                    2.0 * obs_freq_df[, strata]))) > 0)
                # More obs in OOB compared to fit currently
                obs_freq_df[ix, strata] <- floor(obs_freq_df[ix, trn] / 2.0)
            if (length((ix <- which(obs_freq_df[, strata] == 0))) > 0)
                obs_freq_df[ix, strata] <- 1
        }    
        #print(colSums(obs_freq_df[, -1]))
            
#         obs_freq_df <- expand.grid(
#             glb_category_var = ctgry_freq_df[, glb_category_var],
#             glb_rsp_var_raw = rspvar_freq_df[, glb_rsp_var_raw])
#         names(obs_freq_df) <- sample_vars
#         trnobs_freq_df <- orderBy(reformulate(sample_vars),
#                                      mycreate_sqlxtab_df(glb_trnobs_df, sample_vars))
#         names(trnobs_freq_df) <- gsub(".n", ".n.trn", names(trnobs_freq_df), 
#                                       fixed = TRUE)
#         obs_freq_df <- merge(obs_freq_df, trnobs_freq_df, all = TRUE)
#         newobs_freq_df <- orderBy(reformulate(glb_category_var),
#                                 mycreate_sqlxtab_df(glb_newobs_df, glb_category_var))
#         # Since glb_rsp_var_raw might be NA in newobs_df, repeat .n.Test across unique values of glb_rsp_var_raw to prep obs_freq_df
#         newobs_rsp_freq_df <- data.frame()
#         for (val in rspvar_freq_df[, glb_rsp_var_raw])
#             newobs_rsp_freq_df <- rbind(newobs_rsp_freq_df, 
#                             cbind(newobs_freq_df, data.frame(glb_rsp_var_raw = val)))
#         obs_freq_df <- merge(obs_freq_df, newobs_freq_df, all = TRUE)
#         obs_freq_df <- orderBy(reformulate(sample_vars), obs_freq_df)
#         
#         obs_freq_df$.n.strata <- ceiling(
#             as.vector(matrix(ctgry_freq_df$.n.Tst * 1.0 /
#                                  sum(ctgry_freq_df$.n.Tst)) %*%
#                       matrix(OOB_rspvar_size, nrow = 1)))
#         obs_freq_df[obs_freq_df$.n.Strata == 0, ".n.Strata"] <- 1
#         
#         # Adjust OOB_strata_size (desired # of OOB obs) if > # of trn obs
#         ix <- which(!is.na(trnobs_univ_df$.n) & 
#                         (OOB_strata_size > trnobs_univ_df$.n))
#         if (length(ix) > 0)
#             OOB_strata_size[ix] <- trnobs_univ_df[ix, ".n"]
#         # Adjust OOB_strata_size (desired # of OOB obs) if > # of (trn - OOB) obs
#         ix <- which(!is.na(trnobs_univ_df$.n) & 
#                         (OOB_strata_size * 2 > trnobs_univ_df$.n))
#         if (length(ix) > 0)
#             OOB_strata_size[ix] <- trnobs_univ_df[ix, ".n"]
        
        OOB_strata_size <- as.vector(as.matrix(obs_freq_df[, 
                                        grepl("^\\.strata\\.", names(obs_freq_df))]))
        tmp_trnobs_df <- orderBy(reformulate(c(glb_rsp_var_raw, glb_category_var)),
                                glb_trnobs_df)
        require(sampling)
        split_strata <- sampling::strata(tmp_trnobs_df, 
                               stratanames = c(glb_rsp_var_raw, glb_category_var),
                               size = OOB_strata_size[!is.na(OOB_strata_size)],
                               method = "srswor")
        glb_OOBobs_df <- getdata(tmp_trnobs_df, split_strata)[, names(glb_trnobs_df)]
        glb_fitobs_df <- glb_trnobs_df[!glb_trnobs_df[, glb_id_var] %in% 
                                        glb_OOBobs_df[, glb_id_var], ]
    }
} else {
    print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata", 
                  glb_rsp_var))
    glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}
## Loading required package: sampling
## 
## Attaching package: 'sampling'
## 
## The following objects are masked from 'package:survival':
## 
##     cluster, strata
## 
## The following object is masked from 'package:caret':
## 
##     cluster
if (!is.null(glb_max_fitobs) && (nrow(glb_fitobs_df) > glb_max_fitobs)) {
    warning("glb_fitobs_df restricted to glb_max_fitobs: ", 
            format(glb_max_fitobs, big.mark = ","))
    org_fitobs_df <- glb_fitobs_df
    glb_fitobs_df <- 
        org_fitobs_df[split <- sample.split(org_fitobs_df[, glb_rsp_var_raw], 
                                            SplitRatio = glb_max_fitobs), ]
    org_fitobs_df <- NULL
}

glb_allobs_df$.lcn <- ""; glb_trnobs_df$.lcn <- "";
glb_allobs_df[glb_allobs_df[, glb_id_var] %in% 
              glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_trnobs_df[glb_trnobs_df[, glb_id_var] %in% 
              glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_var] %in% 
              glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
glb_trnobs_df[glb_trnobs_df[, glb_id_var] %in% 
              glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"

dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
    xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
    rownames(xtab_df) <- xtab_df[, location_var]
    xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
    print(xtab_df)
    print(xtab_df / rowSums(xtab_df, na.rm=TRUE))    
}    

# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
if (!is.null(glb_category_var)) {
    if (glb_is_classification)
        dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)
    newobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"), 
                                           glb_category_var)
    names(newobs_ctgry_df) <- 
        gsub(".n", ".n.Tst", names(newobs_ctgry_df), fixed = TRUE)
    OOBobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"), 
                                           glb_category_var)
    names(OOBobs_ctgry_df) <- 
        gsub(".n", ".n.OOB", names(OOBobs_ctgry_df), fixed = TRUE)
    fitobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "Fit"), 
                                           glb_category_var)
    names(fitobs_ctgry_df) <- 
        gsub(".n", ".n.Fit", names(fitobs_ctgry_df), fixed = TRUE)
    
#     glb_ctgry_df <- merge(newobs_ctgry_df, OOBobs_ctgry_df, by=glb_category_var
#                           , all=TRUE, suffixes=c(".Tst", ".OOB"))
    glb_ctgry_df <- merge(fitobs_ctgry_df, OOBobs_ctgry_df, by = glb_category_var
                          , all = TRUE)
    glb_ctgry_df <- merge(glb_ctgry_df, newobs_ctgry_df, by = glb_category_var
                          , all = TRUE)
    glb_ctgry_df$.freqRatio.Fit <- 
        glb_ctgry_df$.n.Fit / sum(glb_ctgry_df$.n.Fit, na.rm = TRUE)
    glb_ctgry_df$.freqRatio.OOB <- 
        glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm = TRUE)
    glb_ctgry_df$.freqRatio.Tst <- 
        glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm = TRUE)
    print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB-.freqRatio.Fit, glb_ctgry_df))
}
##     sold.0 sold.1 sold.NA
##         NA     NA     798
## Fit    548    472      NA
## OOB    447    383      NA
##        sold.0    sold.1 sold.NA
##            NA        NA       1
## Fit 0.5372549 0.4627451      NA
## OOB 0.5385542 0.4614458      NA
##    prdl.my.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 3         iPad2    144    142    154     0.14117647     0.17108434
## 8      iPadmini    154    123    111     0.15098039     0.14819277
## 1       Unknown    108     96     92     0.10588235     0.11566265
## 2         iPad1    130     95     88     0.12745098     0.11445783
## 6       iPadAir     98     82     74     0.09607843     0.09879518
## 5         iPad4     84     73     68     0.08235294     0.08795181
## 7      iPadAir2    102     69     62     0.10000000     0.08313253
## 9     iPadmini2     54     53     56     0.05294118     0.06385542
## 4         iPad3     92     61     55     0.09019608     0.07349398
## 10    iPadmini3     54     36     38     0.05294118     0.04337349
##    .freqRatio.Tst
## 3      0.19298246
## 8      0.13909774
## 1      0.11528822
## 2      0.11027569
## 6      0.09273183
## 5      0.08521303
## 7      0.07769424
## 9      0.07017544
## 4      0.06892231
## 10     0.04761905
print("glb_allobs_df: "); print(dim(glb_allobs_df))
## [1] "glb_allobs_df: "
## [1] 2648  105
print("glb_trnobs_df: "); print(dim(glb_trnobs_df))
## [1] "glb_trnobs_df: "
## [1] 1850  105
print("glb_fitobs_df: "); print(dim(glb_fitobs_df))
## [1] "glb_fitobs_df: "
## [1] 1020  104
print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))
## [1] "glb_OOBobs_df: "
## [1] 830 104
print("glb_newobs_df: "); print(dim(glb_newobs_df))
## [1] "glb_newobs_df: "
## [1] 798 104
# # Does not handle NULL or length(glb_id_var) > 1

if (glb_save_envir)
    save(glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
         file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))

rm(split)
## Warning in rm(split): object 'split' not found
glb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)
##                     label step_major step_minor label_minor     bgn
## 8 partition.data.training          4          0           0 330.238
## 9         select.features          5          0           0 331.640
##       end elapsed
## 8 331.639   1.401
## 9      NA      NA

Step 5.0: select features

#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df, 
                       exclude_vars_as_features=glbFeatsExclude, 
                       rsp_var=glb_rsp_var))
## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero
##                                                        id        cor.y
## sold                                                 sold  1.000000000
## biddable                                         biddable  0.549242842
## sprice.root2                                 sprice.root2 -0.511275385
## sprice.log10                                 sprice.log10 -0.469398937
## startprice                                     startprice -0.458981936
## startprice.log10.predict         startprice.log10.predict -0.408722051
## sprice.d20nexp                             sprice.d20nexp  0.397995133
## spdiff.cut.fctr                           spdiff.cut.fctr  0.290604966
## sprice.predict.diff                   sprice.predict.diff  0.271458109
## UniqueID                                         UniqueID -0.190626150
## condition.fctr                             condition.fctr -0.155283820
## startprice.dgt1.is9                   startprice.dgt1.is9 -0.138476687
## D.T.hous                                         D.T.hous -0.129274878
## D.chrs.pnct05.n.log                   D.chrs.pnct05.n.log -0.120173065
## D.T.X100                                         D.T.X100 -0.111398502
## D.chrs.pnct06.n.log                   D.chrs.pnct06.n.log -0.095888068
## D.T.cosmet                                     D.T.cosmet -0.088480863
## D.dgts.n.log                                 D.dgts.n.log -0.082741631
## .clusterid                                     .clusterid -0.080966421
## .clusterid.fctr                           .clusterid.fctr -0.080966421
## D.chrs.pnct14.n.log                   D.chrs.pnct14.n.log -0.075911474
## D.terms.post.stop.n                   D.terms.post.stop.n -0.075731613
## D.terms.post.stem.n                   D.terms.post.stem.n -0.074990216
## cellular.fctr                               cellular.fctr -0.073025939
## D.T.in.                                           D.T.in. -0.071653919
## D.T.with                                         D.T.with -0.066087601
## D.terms.post.stop.n.log           D.terms.post.stop.n.log -0.062526435
## D.terms.post.stem.n.log           D.terms.post.stem.n.log -0.062220631
## D.wrds.unq.n.log                         D.wrds.unq.n.log -0.062220631
## D.chrs.pnct09.n.log                   D.chrs.pnct09.n.log -0.061919863
## D.ratio.wrds.stop.n.wrds.n     D.ratio.wrds.stop.n.wrds.n  0.059547328
## carrier.fctr                                 carrier.fctr -0.058701911
## D.T.mint                                         D.T.mint -0.055691207
## D.wrds.n.log                                 D.wrds.n.log -0.055678363
## D.chrs.n.log                                 D.chrs.n.log -0.055576665
## D.chrs.uppr.n.log                       D.chrs.uppr.n.log -0.054491610
## prdl.my.fctr                                 prdl.my.fctr -0.054151937
## D.chrs.pnct28.n.log                   D.chrs.pnct28.n.log -0.052538378
## D.T.of                                             D.T.of  0.052259530
## D.chrs.pnct12.n.log                   D.chrs.pnct12.n.log -0.049684867
## D.chrs.pnct15.n.log                   D.chrs.pnct15.n.log  0.048611857
## startprice.dcm2.is9                   startprice.dcm2.is9  0.047716416
## D.weight.post.stem.sum             D.weight.post.stem.sum -0.047105442
## D.weight.sum                                 D.weight.sum -0.047105442
## D.weight.post.stop.sum             D.weight.post.stop.sum -0.045009208
## color.fctr                                     color.fctr -0.044231522
## D.terms.n.stem.stop.Ratio       D.terms.n.stem.stop.Ratio  0.043562113
## startprice.dgt3.is9                   startprice.dgt3.is9 -0.043150483
## D.T.minor                                       D.T.minor -0.042692360
## D.T.screen                                     D.T.screen  0.040605274
## startprice.dgt2.is9                   startprice.dgt2.is9 -0.039616843
## D.chrs.pnct08.n.log                   D.chrs.pnct08.n.log -0.039606364
## D.T.condit                                     D.T.condit -0.033248924
## D.T.light                                       D.T.light -0.032814746
## D.chrs.pnct13.n.log                   D.chrs.pnct13.n.log -0.032095777
## D.T.from                                         D.T.from -0.030653359
## D.chrs.pnct17.n.log                   D.chrs.pnct17.n.log  0.025087653
## D.chrs.pnct07.n.log                   D.chrs.pnct07.n.log  0.024330390
## D.chrs.pnct10.n.log                   D.chrs.pnct10.n.log -0.024107007
## D.T.or                                             D.T.or -0.022614420
## D.chrs.pnct03.n.log                   D.chrs.pnct03.n.log -0.021557732
## D.chrs.pnct18.n.log                   D.chrs.pnct18.n.log -0.021557732
## D.P.gold                                         D.P.gold -0.021557732
## D.chrs.pnct11.n.log                   D.chrs.pnct11.n.log -0.020186238
## D.P.white                                       D.P.white  0.018597281
## D.T.good                                         D.T.good  0.016394675
## D.chrs.pnct16.n.log                   D.chrs.pnct16.n.log  0.015642041
## storage.fctr                                 storage.fctr -0.014549906
## D.P.air                                           D.P.air -0.009203749
## startprice.dcm1.is9                   startprice.dcm1.is9 -0.008364097
## D.P.mini                                         D.P.mini -0.006894908
## D.chrs.pnct01.n.log                   D.chrs.pnct01.n.log  0.004291712
## D.wrds.stop.n.log                       D.wrds.stop.n.log  0.003851183
## D.P.spacegray                               D.P.spacegray  0.003532788
## D.T.use                                           D.T.use  0.003025811
## D.weight.sum.stem.stop.Ratio D.weight.sum.stem.stop.Ratio  0.002381527
## .rnorm                                             .rnorm -0.001273806
## D.P.black                                       D.P.black -0.001181544
## D.ratio.weight.sum.wrds.n       D.ratio.weight.sum.wrds.n -0.001080251
## D.chrs.pnct02.n.log                   D.chrs.pnct02.n.log           NA
## D.chrs.pnct04.n.log                   D.chrs.pnct04.n.log           NA
## D.chrs.pnct19.n.log                   D.chrs.pnct19.n.log           NA
## D.chrs.pnct20.n.log                   D.chrs.pnct20.n.log           NA
## D.chrs.pnct21.n.log                   D.chrs.pnct21.n.log           NA
## D.chrs.pnct22.n.log                   D.chrs.pnct22.n.log           NA
## D.chrs.pnct23.n.log                   D.chrs.pnct23.n.log           NA
## D.chrs.pnct24.n.log                   D.chrs.pnct24.n.log           NA
## D.chrs.pnct25.n.log                   D.chrs.pnct25.n.log           NA
## D.chrs.pnct26.n.log                   D.chrs.pnct26.n.log           NA
## D.chrs.pnct27.n.log                   D.chrs.pnct27.n.log           NA
## D.chrs.pnct29.n.log                   D.chrs.pnct29.n.log           NA
## D.chrs.pnct30.n.log                   D.chrs.pnct30.n.log           NA
## D.P.http                                         D.P.http           NA
##                              exclude.as.feat   cor.y.abs
## sold                                       1 1.000000000
## biddable                                   0 0.549242842
## sprice.root2                               0 0.511275385
## sprice.log10                               0 0.469398937
## startprice                                 1 0.458981936
## startprice.log10.predict                   1 0.408722051
## sprice.d20nexp                             0 0.397995133
## spdiff.cut.fctr                            0 0.290604966
## sprice.predict.diff                        1 0.271458109
## UniqueID                                   1 0.190626150
## condition.fctr                             0 0.155283820
## startprice.dgt1.is9                        0 0.138476687
## D.T.hous                                   0 0.129274878
## D.chrs.pnct05.n.log                        0 0.120173065
## D.T.X100                                   0 0.111398502
## D.chrs.pnct06.n.log                        0 0.095888068
## D.T.cosmet                                 0 0.088480863
## D.dgts.n.log                               0 0.082741631
## .clusterid                                 1 0.080966421
## .clusterid.fctr                            0 0.080966421
## D.chrs.pnct14.n.log                        0 0.075911474
## D.terms.post.stop.n                        1 0.075731613
## D.terms.post.stem.n                        1 0.074990216
## cellular.fctr                              0 0.073025939
## D.T.in.                                    0 0.071653919
## D.T.with                                   0 0.066087601
## D.terms.post.stop.n.log                    0 0.062526435
## D.terms.post.stem.n.log                    0 0.062220631
## D.wrds.unq.n.log                           0 0.062220631
## D.chrs.pnct09.n.log                        0 0.061919863
## D.ratio.wrds.stop.n.wrds.n                 0 0.059547328
## carrier.fctr                               0 0.058701911
## D.T.mint                                   0 0.055691207
## D.wrds.n.log                               0 0.055678363
## D.chrs.n.log                               0 0.055576665
## D.chrs.uppr.n.log                          0 0.054491610
## prdl.my.fctr                               0 0.054151937
## D.chrs.pnct28.n.log                        0 0.052538378
## D.T.of                                     0 0.052259530
## D.chrs.pnct12.n.log                        0 0.049684867
## D.chrs.pnct15.n.log                        0 0.048611857
## startprice.dcm2.is9                        0 0.047716416
## D.weight.post.stem.sum                     0 0.047105442
## D.weight.sum                               0 0.047105442
## D.weight.post.stop.sum                     0 0.045009208
## color.fctr                                 0 0.044231522
## D.terms.n.stem.stop.Ratio                  0 0.043562113
## startprice.dgt3.is9                        0 0.043150483
## D.T.minor                                  0 0.042692360
## D.T.screen                                 0 0.040605274
## startprice.dgt2.is9                        0 0.039616843
## D.chrs.pnct08.n.log                        0 0.039606364
## D.T.condit                                 0 0.033248924
## D.T.light                                  0 0.032814746
## D.chrs.pnct13.n.log                        0 0.032095777
## D.T.from                                   0 0.030653359
## D.chrs.pnct17.n.log                        0 0.025087653
## D.chrs.pnct07.n.log                        0 0.024330390
## D.chrs.pnct10.n.log                        0 0.024107007
## D.T.or                                     0 0.022614420
## D.chrs.pnct03.n.log                        0 0.021557732
## D.chrs.pnct18.n.log                        0 0.021557732
## D.P.gold                                   0 0.021557732
## D.chrs.pnct11.n.log                        0 0.020186238
## D.P.white                                  0 0.018597281
## D.T.good                                   0 0.016394675
## D.chrs.pnct16.n.log                        0 0.015642041
## storage.fctr                               0 0.014549906
## D.P.air                                    0 0.009203749
## startprice.dcm1.is9                        0 0.008364097
## D.P.mini                                   0 0.006894908
## D.chrs.pnct01.n.log                        0 0.004291712
## D.wrds.stop.n.log                          0 0.003851183
## D.P.spacegray                              0 0.003532788
## D.T.use                                    0 0.003025811
## D.weight.sum.stem.stop.Ratio               0 0.002381527
## .rnorm                                     0 0.001273806
## D.P.black                                  0 0.001181544
## D.ratio.weight.sum.wrds.n                  0 0.001080251
## D.chrs.pnct02.n.log                        0          NA
## D.chrs.pnct04.n.log                        0          NA
## D.chrs.pnct19.n.log                        0          NA
## D.chrs.pnct20.n.log                        0          NA
## D.chrs.pnct21.n.log                        0          NA
## D.chrs.pnct22.n.log                        0          NA
## D.chrs.pnct23.n.log                        0          NA
## D.chrs.pnct24.n.log                        0          NA
## D.chrs.pnct25.n.log                        0          NA
## D.chrs.pnct26.n.log                        0          NA
## D.chrs.pnct27.n.log                        0          NA
## D.chrs.pnct29.n.log                        0          NA
## D.chrs.pnct30.n.log                        0          NA
## D.P.http                                   0          NA
print(glb_feats_df <- orderBy(~-cor.y, 
          myfind_cor_features(feats_df=glb_feats_df, obs_df=glb_trnobs_df, rsp_var=glb_rsp_var,
                              nzv.freqCut=glb_nzv_freqCut, nzv.uniqueCut=glb_nzv_uniqueCut)))
## [1] "cor(D.terms.post.stem.n.log, D.wrds.unq.n.log)=1.0000"
## [1] "cor(sold.fctr, D.terms.post.stem.n.log)=-0.0622"
## [1] "cor(sold.fctr, D.wrds.unq.n.log)=-0.0622"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.wrds.unq.n.log as highly correlated with
## D.terms.post.stem.n.log
## [1] "cor(D.weight.post.stem.sum, D.weight.sum)=1.0000"
## [1] "cor(sold.fctr, D.weight.post.stem.sum)=-0.0471"
## [1] "cor(sold.fctr, D.weight.sum)=-0.0471"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.weight.sum as highly correlated with
## D.weight.post.stem.sum
## [1] "cor(D.terms.post.stem.n.log, D.terms.post.stop.n.log)=0.9999"
## [1] "cor(sold.fctr, D.terms.post.stem.n.log)=-0.0622"
## [1] "cor(sold.fctr, D.terms.post.stop.n.log)=-0.0625"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.post.stem.n.log as highly correlated
## with D.terms.post.stop.n.log
## [1] "cor(D.chrs.n.log, D.chrs.uppr.n.log)=0.9996"
## [1] "cor(sold.fctr, D.chrs.n.log)=-0.0556"
## [1] "cor(sold.fctr, D.chrs.uppr.n.log)=-0.0545"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.chrs.uppr.n.log as highly correlated with
## D.chrs.n.log
## [1] "cor(D.weight.post.stem.sum, D.weight.post.stop.sum)=0.9981"
## [1] "cor(sold.fctr, D.weight.post.stem.sum)=-0.0471"
## [1] "cor(sold.fctr, D.weight.post.stop.sum)=-0.0450"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.weight.post.stop.sum as highly correlated
## with D.weight.post.stem.sum
## [1] "cor(D.terms.post.stop.n.log, D.wrds.n.log)=0.9956"
## [1] "cor(sold.fctr, D.terms.post.stop.n.log)=-0.0625"
## [1] "cor(sold.fctr, D.wrds.n.log)=-0.0557"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.wrds.n.log as highly correlated with
## D.terms.post.stop.n.log
## [1] "cor(D.chrs.n.log, D.terms.post.stop.n.log)=0.9889"
## [1] "cor(sold.fctr, D.chrs.n.log)=-0.0556"
## [1] "cor(sold.fctr, D.terms.post.stop.n.log)=-0.0625"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.chrs.n.log as highly correlated with
## D.terms.post.stop.n.log
## [1] "cor(D.ratio.wrds.stop.n.wrds.n, D.terms.post.stop.n.log)=-0.9745"
## [1] "cor(sold.fctr, D.ratio.wrds.stop.n.wrds.n)=0.0595"
## [1] "cor(sold.fctr, D.terms.post.stop.n.log)=-0.0625"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.ratio.wrds.stop.n.wrds.n as highly correlated
## with D.terms.post.stop.n.log
## [1] "cor(sprice.d20nexp, sprice.log10)=-0.9316"
## [1] "cor(sold.fctr, sprice.d20nexp)=0.3980"
## [1] "cor(sold.fctr, sprice.log10)=-0.4694"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified sprice.d20nexp as highly correlated with
## sprice.log10
## [1] "cor(D.terms.post.stop.n.log, D.weight.post.stem.sum)=0.9316"
## [1] "cor(sold.fctr, D.terms.post.stop.n.log)=-0.0625"
## [1] "cor(sold.fctr, D.weight.post.stem.sum)=-0.0471"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.weight.post.stem.sum as highly correlated
## with D.terms.post.stop.n.log
## [1] "cor(D.T.X100, D.chrs.pnct05.n.log)=0.8813"
## [1] "cor(sold.fctr, D.T.X100)=-0.1114"
## [1] "cor(sold.fctr, D.chrs.pnct05.n.log)=-0.1202"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df
## = glb_trnobs_df, : Identified D.T.X100 as highly correlated with
## D.chrs.pnct05.n.log
## [1] "cor(sprice.log10, sprice.root2)=0.8701"
## [1] "cor(sold.fctr, sprice.log10)=-0.4694"
## [1] "cor(sold.fctr, sprice.root2)=-0.5113"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified sprice.log10 as highly correlated with
## sprice.root2
## [1] "cor(startprice.dcm1.is9, startprice.dcm2.is9)=0.8054"
## [1] "cor(sold.fctr, startprice.dcm1.is9)=-0.0084"
## [1] "cor(sold.fctr, startprice.dcm2.is9)=0.0477"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified startprice.dcm1.is9 as highly correlated with
## startprice.dcm2.is9
## [1] "cor(D.chrs.pnct13.n.log, D.terms.post.stop.n.log)=0.7218"
## [1] "cor(sold.fctr, D.chrs.pnct13.n.log)=-0.0321"
## [1] "cor(sold.fctr, D.terms.post.stop.n.log)=-0.0625"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.chrs.pnct13.n.log as highly correlated with
## D.terms.post.stop.n.log
## [1] "cor(carrier.fctr, cellular.fctr)=0.7131"
## [1] "cor(sold.fctr, carrier.fctr)=-0.0587"
## [1] "cor(sold.fctr, cellular.fctr)=-0.0730"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified carrier.fctr as highly correlated with
## cellular.fctr
##                                                        id        cor.y
## sold                                                 sold  1.000000000
## biddable                                         biddable  0.549242842
## sprice.d20nexp                             sprice.d20nexp  0.397995133
## spdiff.cut.fctr                           spdiff.cut.fctr  0.290604966
## sprice.predict.diff                   sprice.predict.diff  0.271458109
## D.ratio.wrds.stop.n.wrds.n     D.ratio.wrds.stop.n.wrds.n  0.059547328
## D.T.of                                             D.T.of  0.052259530
## D.chrs.pnct15.n.log                   D.chrs.pnct15.n.log  0.048611857
## startprice.dcm2.is9                   startprice.dcm2.is9  0.047716416
## D.terms.n.stem.stop.Ratio       D.terms.n.stem.stop.Ratio  0.043562113
## D.T.screen                                     D.T.screen  0.040605274
## D.chrs.pnct17.n.log                   D.chrs.pnct17.n.log  0.025087653
## D.chrs.pnct07.n.log                   D.chrs.pnct07.n.log  0.024330390
## D.P.white                                       D.P.white  0.018597281
## D.T.good                                         D.T.good  0.016394675
## D.chrs.pnct16.n.log                   D.chrs.pnct16.n.log  0.015642041
## D.chrs.pnct01.n.log                   D.chrs.pnct01.n.log  0.004291712
## D.wrds.stop.n.log                       D.wrds.stop.n.log  0.003851183
## D.P.spacegray                               D.P.spacegray  0.003532788
## D.T.use                                           D.T.use  0.003025811
## D.weight.sum.stem.stop.Ratio D.weight.sum.stem.stop.Ratio  0.002381527
## D.ratio.weight.sum.wrds.n       D.ratio.weight.sum.wrds.n -0.001080251
## D.P.black                                       D.P.black -0.001181544
## .rnorm                                             .rnorm -0.001273806
## D.P.mini                                         D.P.mini -0.006894908
## startprice.dcm1.is9                   startprice.dcm1.is9 -0.008364097
## D.P.air                                           D.P.air -0.009203749
## storage.fctr                                 storage.fctr -0.014549906
## D.chrs.pnct11.n.log                   D.chrs.pnct11.n.log -0.020186238
## D.P.gold                                         D.P.gold -0.021557732
## D.chrs.pnct03.n.log                   D.chrs.pnct03.n.log -0.021557732
## D.chrs.pnct18.n.log                   D.chrs.pnct18.n.log -0.021557732
## D.T.or                                             D.T.or -0.022614420
## D.chrs.pnct10.n.log                   D.chrs.pnct10.n.log -0.024107007
## D.T.from                                         D.T.from -0.030653359
## D.chrs.pnct13.n.log                   D.chrs.pnct13.n.log -0.032095777
## D.T.light                                       D.T.light -0.032814746
## D.T.condit                                     D.T.condit -0.033248924
## D.chrs.pnct08.n.log                   D.chrs.pnct08.n.log -0.039606364
## startprice.dgt2.is9                   startprice.dgt2.is9 -0.039616843
## D.T.minor                                       D.T.minor -0.042692360
## startprice.dgt3.is9                   startprice.dgt3.is9 -0.043150483
## color.fctr                                     color.fctr -0.044231522
## D.weight.post.stop.sum             D.weight.post.stop.sum -0.045009208
## D.weight.post.stem.sum             D.weight.post.stem.sum -0.047105442
## D.weight.sum                                 D.weight.sum -0.047105442
## D.chrs.pnct12.n.log                   D.chrs.pnct12.n.log -0.049684867
## D.chrs.pnct28.n.log                   D.chrs.pnct28.n.log -0.052538378
## prdl.my.fctr                                 prdl.my.fctr -0.054151937
## D.chrs.uppr.n.log                       D.chrs.uppr.n.log -0.054491610
## D.chrs.n.log                                 D.chrs.n.log -0.055576665
## D.wrds.n.log                                 D.wrds.n.log -0.055678363
## D.T.mint                                         D.T.mint -0.055691207
## carrier.fctr                                 carrier.fctr -0.058701911
## D.chrs.pnct09.n.log                   D.chrs.pnct09.n.log -0.061919863
## D.terms.post.stem.n.log           D.terms.post.stem.n.log -0.062220631
## D.wrds.unq.n.log                         D.wrds.unq.n.log -0.062220631
## D.terms.post.stop.n.log           D.terms.post.stop.n.log -0.062526435
## D.T.with                                         D.T.with -0.066087601
## D.T.in.                                           D.T.in. -0.071653919
## cellular.fctr                               cellular.fctr -0.073025939
## D.terms.post.stem.n                   D.terms.post.stem.n -0.074990216
## D.terms.post.stop.n                   D.terms.post.stop.n -0.075731613
## D.chrs.pnct14.n.log                   D.chrs.pnct14.n.log -0.075911474
## .clusterid                                     .clusterid -0.080966421
## .clusterid.fctr                           .clusterid.fctr -0.080966421
## D.dgts.n.log                                 D.dgts.n.log -0.082741631
## D.T.cosmet                                     D.T.cosmet -0.088480863
## D.chrs.pnct06.n.log                   D.chrs.pnct06.n.log -0.095888068
## D.T.X100                                         D.T.X100 -0.111398502
## D.chrs.pnct05.n.log                   D.chrs.pnct05.n.log -0.120173065
## D.T.hous                                         D.T.hous -0.129274878
## startprice.dgt1.is9                   startprice.dgt1.is9 -0.138476687
## condition.fctr                             condition.fctr -0.155283820
## UniqueID                                         UniqueID -0.190626150
## startprice.log10.predict         startprice.log10.predict -0.408722051
## startprice                                     startprice -0.458981936
## sprice.log10                                 sprice.log10 -0.469398937
## sprice.root2                                 sprice.root2 -0.511275385
## D.P.http                                         D.P.http           NA
## D.chrs.pnct02.n.log                   D.chrs.pnct02.n.log           NA
## D.chrs.pnct04.n.log                   D.chrs.pnct04.n.log           NA
## D.chrs.pnct19.n.log                   D.chrs.pnct19.n.log           NA
## D.chrs.pnct20.n.log                   D.chrs.pnct20.n.log           NA
## D.chrs.pnct21.n.log                   D.chrs.pnct21.n.log           NA
## D.chrs.pnct22.n.log                   D.chrs.pnct22.n.log           NA
## D.chrs.pnct23.n.log                   D.chrs.pnct23.n.log           NA
## D.chrs.pnct24.n.log                   D.chrs.pnct24.n.log           NA
## D.chrs.pnct25.n.log                   D.chrs.pnct25.n.log           NA
## D.chrs.pnct26.n.log                   D.chrs.pnct26.n.log           NA
## D.chrs.pnct27.n.log                   D.chrs.pnct27.n.log           NA
## D.chrs.pnct29.n.log                   D.chrs.pnct29.n.log           NA
## D.chrs.pnct30.n.log                   D.chrs.pnct30.n.log           NA
##                              exclude.as.feat   cor.y.abs
## sold                                       1 1.000000000
## biddable                                   0 0.549242842
## sprice.d20nexp                             0 0.397995133
## spdiff.cut.fctr                            0 0.290604966
## sprice.predict.diff                        1 0.271458109
## D.ratio.wrds.stop.n.wrds.n                 0 0.059547328
## D.T.of                                     0 0.052259530
## D.chrs.pnct15.n.log                        0 0.048611857
## startprice.dcm2.is9                        0 0.047716416
## D.terms.n.stem.stop.Ratio                  0 0.043562113
## D.T.screen                                 0 0.040605274
## D.chrs.pnct17.n.log                        0 0.025087653
## D.chrs.pnct07.n.log                        0 0.024330390
## D.P.white                                  0 0.018597281
## D.T.good                                   0 0.016394675
## D.chrs.pnct16.n.log                        0 0.015642041
## D.chrs.pnct01.n.log                        0 0.004291712
## D.wrds.stop.n.log                          0 0.003851183
## D.P.spacegray                              0 0.003532788
## D.T.use                                    0 0.003025811
## D.weight.sum.stem.stop.Ratio               0 0.002381527
## D.ratio.weight.sum.wrds.n                  0 0.001080251
## D.P.black                                  0 0.001181544
## .rnorm                                     0 0.001273806
## D.P.mini                                   0 0.006894908
## startprice.dcm1.is9                        0 0.008364097
## D.P.air                                    0 0.009203749
## storage.fctr                               0 0.014549906
## D.chrs.pnct11.n.log                        0 0.020186238
## D.P.gold                                   0 0.021557732
## D.chrs.pnct03.n.log                        0 0.021557732
## D.chrs.pnct18.n.log                        0 0.021557732
## D.T.or                                     0 0.022614420
## D.chrs.pnct10.n.log                        0 0.024107007
## D.T.from                                   0 0.030653359
## D.chrs.pnct13.n.log                        0 0.032095777
## D.T.light                                  0 0.032814746
## D.T.condit                                 0 0.033248924
## D.chrs.pnct08.n.log                        0 0.039606364
## startprice.dgt2.is9                        0 0.039616843
## D.T.minor                                  0 0.042692360
## startprice.dgt3.is9                        0 0.043150483
## color.fctr                                 0 0.044231522
## D.weight.post.stop.sum                     0 0.045009208
## D.weight.post.stem.sum                     0 0.047105442
## D.weight.sum                               0 0.047105442
## D.chrs.pnct12.n.log                        0 0.049684867
## D.chrs.pnct28.n.log                        0 0.052538378
## prdl.my.fctr                               0 0.054151937
## D.chrs.uppr.n.log                          0 0.054491610
## D.chrs.n.log                               0 0.055576665
## D.wrds.n.log                               0 0.055678363
## D.T.mint                                   0 0.055691207
## carrier.fctr                               0 0.058701911
## D.chrs.pnct09.n.log                        0 0.061919863
## D.terms.post.stem.n.log                    0 0.062220631
## D.wrds.unq.n.log                           0 0.062220631
## D.terms.post.stop.n.log                    0 0.062526435
## D.T.with                                   0 0.066087601
## D.T.in.                                    0 0.071653919
## cellular.fctr                              0 0.073025939
## D.terms.post.stem.n                        1 0.074990216
## D.terms.post.stop.n                        1 0.075731613
## D.chrs.pnct14.n.log                        0 0.075911474
## .clusterid                                 1 0.080966421
## .clusterid.fctr                            0 0.080966421
## D.dgts.n.log                               0 0.082741631
## D.T.cosmet                                 0 0.088480863
## D.chrs.pnct06.n.log                        0 0.095888068
## D.T.X100                                   0 0.111398502
## D.chrs.pnct05.n.log                        0 0.120173065
## D.T.hous                                   0 0.129274878
## startprice.dgt1.is9                        0 0.138476687
## condition.fctr                             0 0.155283820
## UniqueID                                   1 0.190626150
## startprice.log10.predict                   1 0.408722051
## startprice                                 1 0.458981936
## sprice.log10                               0 0.469398937
## sprice.root2                               0 0.511275385
## D.P.http                                   0          NA
## D.chrs.pnct02.n.log                        0          NA
## D.chrs.pnct04.n.log                        0          NA
## D.chrs.pnct19.n.log                        0          NA
## D.chrs.pnct20.n.log                        0          NA
## D.chrs.pnct21.n.log                        0          NA
## D.chrs.pnct22.n.log                        0          NA
## D.chrs.pnct23.n.log                        0          NA
## D.chrs.pnct24.n.log                        0          NA
## D.chrs.pnct25.n.log                        0          NA
## D.chrs.pnct26.n.log                        0          NA
## D.chrs.pnct27.n.log                        0          NA
## D.chrs.pnct29.n.log                        0          NA
## D.chrs.pnct30.n.log                        0          NA
##                                           cor.high.X   freqRatio
## sold                                            <NA>    1.163743
## biddable                                        <NA>    1.215569
## sprice.d20nexp                          sprice.log10    2.807692
## spdiff.cut.fctr                                 <NA>    1.668605
## sprice.predict.diff                             <NA>    1.000000
## D.ratio.wrds.stop.n.wrds.n   D.terms.post.stop.n.log   18.067797
## D.T.of                                          <NA>  145.000000
## D.chrs.pnct15.n.log                             <NA>  152.666667
## startprice.dcm2.is9                             <NA>    2.195164
## D.terms.n.stem.stop.Ratio                       <NA>   85.142857
## D.T.screen                                      <NA>   70.916667
## D.chrs.pnct17.n.log                             <NA> 1849.000000
## D.chrs.pnct07.n.log                             <NA>  107.529412
## D.P.white                                       <NA>  230.125000
## D.T.good                                        <NA>   95.166667
## D.chrs.pnct16.n.log                             <NA>  263.142857
## D.chrs.pnct01.n.log                             <NA>   52.705882
## D.wrds.stop.n.log                               <NA>    8.732484
## D.P.spacegray                                   <NA>  461.500000
## D.T.use                                         <NA>   86.631579
## D.weight.sum.stem.stop.Ratio                    <NA>   64.470588
## D.ratio.weight.sum.wrds.n                       <NA>   62.705882
## D.P.black                                       <NA>  167.181818
## .rnorm                                          <NA>    1.000000
## D.P.mini                                        <NA>   96.315789
## startprice.dcm1.is9              startprice.dcm2.is9    1.616690
## D.P.air                                         <NA>  122.266667
## storage.fctr                                    <NA>    2.750742
## D.chrs.pnct11.n.log                             <NA>    9.382353
## D.P.gold                                        <NA> 1849.000000
## D.chrs.pnct03.n.log                             <NA> 1849.000000
## D.chrs.pnct18.n.log                             <NA> 1849.000000
## D.T.or                                          <NA>   87.950000
## D.chrs.pnct10.n.log                             <NA>  307.166667
## D.T.from                                        <NA>   95.315789
## D.chrs.pnct13.n.log          D.terms.post.stop.n.log    5.272374
## D.T.light                                       <NA>   99.277778
## D.T.condit                                      <NA>   36.829268
## D.chrs.pnct08.n.log                             <NA>   69.230769
## startprice.dgt2.is9                             <NA>    6.905983
## D.T.minor                                       <NA>   92.526316
## startprice.dgt3.is9                             <NA>  461.500000
## color.fctr                                      <NA>    1.592342
## D.weight.post.stop.sum        D.weight.post.stem.sum   62.705882
## D.weight.post.stem.sum       D.terms.post.stop.n.log   62.705882
## D.weight.sum                  D.weight.post.stem.sum   62.705882
## D.chrs.pnct12.n.log                             <NA>   28.467742
## D.chrs.pnct28.n.log                             <NA>  461.000000
## prdl.my.fctr                                    <NA>    1.032491
## D.chrs.uppr.n.log                       D.chrs.n.log   15.661765
## D.chrs.n.log                 D.terms.post.stop.n.log   13.455696
## D.wrds.n.log                 D.terms.post.stop.n.log   11.430108
## D.T.mint                                        <NA>   94.894737
## carrier.fctr                           cellular.fctr    3.184971
## D.chrs.pnct09.n.log                             <NA>  306.833333
## D.terms.post.stem.n.log      D.terms.post.stop.n.log   11.714286
## D.wrds.unq.n.log             D.terms.post.stem.n.log   11.714286
## D.terms.post.stop.n.log                         <NA>   13.325000
## D.T.with                                        <NA>   46.888889
## D.T.in.                                         <NA>   33.521739
## cellular.fctr                                   <NA>    2.103053
## D.terms.post.stem.n                             <NA>   11.714286
## D.terms.post.stop.n                             <NA>   13.325000
## D.chrs.pnct14.n.log                             <NA>   35.176471
## .clusterid                                      <NA>   10.959732
## .clusterid.fctr                                 <NA>   10.959732
## D.dgts.n.log                                    <NA>   34.340000
## D.T.cosmet                                      <NA>   88.400000
## D.chrs.pnct06.n.log                             <NA>   60.466667
## D.T.X100                         D.chrs.pnct05.n.log  180.500000
## D.chrs.pnct05.n.log                             <NA>   39.217391
## D.T.hous                                        <NA>   81.681818
## startprice.dgt1.is9                             <NA>    2.042763
## condition.fctr                                  <NA>    4.010453
## UniqueID                                        <NA>    1.000000
## startprice.log10.predict                        <NA>    1.230769
## startprice                                      <NA>    2.807692
## sprice.log10                            sprice.root2    2.807692
## sprice.root2                                    <NA>    2.807692
## D.P.http                                        <NA>    0.000000
## D.chrs.pnct02.n.log                             <NA>    0.000000
## D.chrs.pnct04.n.log                             <NA>    0.000000
## D.chrs.pnct19.n.log                             <NA>    0.000000
## D.chrs.pnct20.n.log                             <NA>    0.000000
## D.chrs.pnct21.n.log                             <NA>    0.000000
## D.chrs.pnct22.n.log                             <NA>    0.000000
## D.chrs.pnct23.n.log                             <NA>    0.000000
## D.chrs.pnct24.n.log                             <NA>    0.000000
## D.chrs.pnct25.n.log                             <NA>    0.000000
## D.chrs.pnct26.n.log                             <NA>    0.000000
## D.chrs.pnct27.n.log                             <NA>    0.000000
## D.chrs.pnct29.n.log                             <NA>    0.000000
## D.chrs.pnct30.n.log                             <NA>    0.000000
##                              percentUnique zeroVar   nzv is.cor.y.abs.low
## sold                            0.10810811   FALSE FALSE            FALSE
## biddable                        0.10810811   FALSE FALSE            FALSE
## sprice.d20nexp                 30.21621622   FALSE FALSE            FALSE
## spdiff.cut.fctr                 0.43243243   FALSE FALSE            FALSE
## sprice.predict.diff            96.10810811   FALSE FALSE            FALSE
## D.ratio.wrds.stop.n.wrds.n      4.27027027   FALSE FALSE            FALSE
## D.T.of                          1.13513514   FALSE  TRUE            FALSE
## D.chrs.pnct15.n.log             0.16216216   FALSE  TRUE            FALSE
## startprice.dcm2.is9             0.10810811   FALSE FALSE            FALSE
## D.terms.n.stem.stop.Ratio       0.64864865   FALSE  TRUE            FALSE
## D.T.screen                      1.13513514   FALSE  TRUE            FALSE
## D.chrs.pnct17.n.log             0.10810811   FALSE  TRUE            FALSE
## D.chrs.pnct07.n.log             0.16216216   FALSE  TRUE            FALSE
## D.P.white                       0.16216216   FALSE  TRUE            FALSE
## D.T.good                        1.18918919   FALSE  TRUE            FALSE
## D.chrs.pnct16.n.log             0.16216216   FALSE  TRUE            FALSE
## D.chrs.pnct01.n.log             0.32432432   FALSE  TRUE            FALSE
## D.wrds.stop.n.log               0.54054054   FALSE FALSE            FALSE
## D.P.spacegray                   0.10810811   FALSE  TRUE            FALSE
## D.T.use                         1.51351351   FALSE  TRUE            FALSE
## D.weight.sum.stem.stop.Ratio   33.56756757   FALSE FALSE            FALSE
## D.ratio.weight.sum.wrds.n      34.81081081   FALSE FALSE             TRUE
## D.P.black                       0.10810811   FALSE  TRUE             TRUE
## .rnorm                        100.00000000   FALSE FALSE            FALSE
## D.P.mini                        0.16216216   FALSE  TRUE            FALSE
## startprice.dcm1.is9             0.10810811   FALSE FALSE            FALSE
## D.P.air                         0.16216216   FALSE  TRUE            FALSE
## storage.fctr                    0.27027027   FALSE FALSE            FALSE
## D.chrs.pnct11.n.log             0.37837838   FALSE FALSE            FALSE
## D.P.gold                        0.10810811   FALSE  TRUE            FALSE
## D.chrs.pnct03.n.log             0.10810811   FALSE  TRUE            FALSE
## D.chrs.pnct18.n.log             0.10810811   FALSE  TRUE            FALSE
## D.T.or                          1.08108108   FALSE  TRUE            FALSE
## D.chrs.pnct10.n.log             0.16216216   FALSE  TRUE            FALSE
## D.T.from                        0.70270270   FALSE  TRUE            FALSE
## D.chrs.pnct13.n.log             0.48648649   FALSE FALSE            FALSE
## D.T.light                       0.91891892   FALSE  TRUE            FALSE
## D.T.condit                      1.35135135   FALSE  TRUE            FALSE
## D.chrs.pnct08.n.log             0.21621622   FALSE  TRUE            FALSE
## startprice.dgt2.is9             0.10810811   FALSE FALSE            FALSE
## D.T.minor                       0.97297297   FALSE  TRUE            FALSE
## startprice.dgt3.is9             0.10810811   FALSE  TRUE            FALSE
## color.fctr                      0.27027027   FALSE FALSE            FALSE
## D.weight.post.stop.sum         34.75675676   FALSE FALSE            FALSE
## D.weight.post.stem.sum         34.64864865   FALSE FALSE            FALSE
## D.weight.sum                   34.64864865   FALSE FALSE            FALSE
## D.chrs.pnct12.n.log             0.21621622   FALSE  TRUE            FALSE
## D.chrs.pnct28.n.log             0.16216216   FALSE  TRUE            FALSE
## prdl.my.fctr                    0.54054054   FALSE FALSE            FALSE
## D.chrs.uppr.n.log               4.48648649   FALSE FALSE            FALSE
## D.chrs.n.log                    5.35135135   FALSE FALSE            FALSE
## D.wrds.n.log                    1.29729730   FALSE FALSE            FALSE
## D.T.mint                        1.02702703   FALSE  TRUE            FALSE
## carrier.fctr                    0.37837838   FALSE FALSE            FALSE
## D.chrs.pnct09.n.log             0.21621622   FALSE  TRUE            FALSE
## D.terms.post.stem.n.log         1.13513514   FALSE FALSE            FALSE
## D.wrds.unq.n.log                1.13513514   FALSE FALSE            FALSE
## D.terms.post.stop.n.log         1.13513514   FALSE FALSE            FALSE
## D.T.with                        1.02702703   FALSE  TRUE            FALSE
## D.T.in.                         1.35135135   FALSE  TRUE            FALSE
## cellular.fctr                   0.16216216   FALSE FALSE            FALSE
## D.terms.post.stem.n             1.13513514   FALSE FALSE            FALSE
## D.terms.post.stop.n             1.13513514   FALSE FALSE            FALSE
## D.chrs.pnct14.n.log             0.27027027   FALSE  TRUE            FALSE
## .clusterid                      0.16216216   FALSE FALSE            FALSE
## .clusterid.fctr                 0.16216216   FALSE FALSE            FALSE
## D.dgts.n.log                    0.59459459   FALSE  TRUE            FALSE
## D.T.cosmet                      0.81081081   FALSE  TRUE            FALSE
## D.chrs.pnct06.n.log             0.16216216   FALSE  TRUE            FALSE
## D.T.X100                        0.64864865   FALSE  TRUE            FALSE
## D.chrs.pnct05.n.log             0.10810811   FALSE  TRUE            FALSE
## D.T.hous                        0.54054054   FALSE  TRUE            FALSE
## startprice.dgt1.is9             0.10810811   FALSE FALSE            FALSE
## condition.fctr                  0.32432432   FALSE FALSE            FALSE
## UniqueID                      100.00000000   FALSE FALSE            FALSE
## startprice.log10.predict       87.94594595   FALSE FALSE            FALSE
## startprice                     30.21621622   FALSE FALSE            FALSE
## sprice.log10                   30.21621622   FALSE FALSE            FALSE
## sprice.root2                   30.21621622   FALSE FALSE            FALSE
## D.P.http                        0.05405405    TRUE  TRUE               NA
## D.chrs.pnct02.n.log             0.05405405    TRUE  TRUE               NA
## D.chrs.pnct04.n.log             0.05405405    TRUE  TRUE               NA
## D.chrs.pnct19.n.log             0.05405405    TRUE  TRUE               NA
## D.chrs.pnct20.n.log             0.05405405    TRUE  TRUE               NA
## D.chrs.pnct21.n.log             0.05405405    TRUE  TRUE               NA
## D.chrs.pnct22.n.log             0.05405405    TRUE  TRUE               NA
## D.chrs.pnct23.n.log             0.05405405    TRUE  TRUE               NA
## D.chrs.pnct24.n.log             0.05405405    TRUE  TRUE               NA
## D.chrs.pnct25.n.log             0.05405405    TRUE  TRUE               NA
## D.chrs.pnct26.n.log             0.05405405    TRUE  TRUE               NA
## D.chrs.pnct27.n.log             0.05405405    TRUE  TRUE               NA
## D.chrs.pnct29.n.log             0.05405405    TRUE  TRUE               NA
## D.chrs.pnct30.n.log             0.05405405    TRUE  TRUE               NA
plt_feats_df <- glb_feats_df
print(myplot_scatter(plt_feats_df, "percentUnique", "freqRatio", 
                     colorcol_name="nzv", jitter=TRUE) + 
          #geom_point(aes(shape=nzv)) +           
          geom_point() + 
          xlim(-5, 25) + 
          geom_hline(yintercept=glb_nzv_freqCut) +
          geom_vline(xintercept=glb_nzv_uniqueCut))
## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor
## Warning: Removed 13 rows containing missing values (geom_point).
## Warning: Removed 13 rows containing missing values (geom_point).
## Warning: Removed 13 rows containing missing values (geom_point).

print(subset(glb_feats_df, nzv))
##                                                  id        cor.y
## D.T.of                                       D.T.of  0.052259530
## D.chrs.pnct15.n.log             D.chrs.pnct15.n.log  0.048611857
## D.terms.n.stem.stop.Ratio D.terms.n.stem.stop.Ratio  0.043562113
## D.T.screen                               D.T.screen  0.040605274
## D.chrs.pnct17.n.log             D.chrs.pnct17.n.log  0.025087653
## D.chrs.pnct07.n.log             D.chrs.pnct07.n.log  0.024330390
## D.P.white                                 D.P.white  0.018597281
## D.T.good                                   D.T.good  0.016394675
## D.chrs.pnct16.n.log             D.chrs.pnct16.n.log  0.015642041
## D.chrs.pnct01.n.log             D.chrs.pnct01.n.log  0.004291712
## D.P.spacegray                         D.P.spacegray  0.003532788
## D.T.use                                     D.T.use  0.003025811
## D.P.black                                 D.P.black -0.001181544
## D.P.mini                                   D.P.mini -0.006894908
## D.P.air                                     D.P.air -0.009203749
## D.P.gold                                   D.P.gold -0.021557732
## D.chrs.pnct03.n.log             D.chrs.pnct03.n.log -0.021557732
## D.chrs.pnct18.n.log             D.chrs.pnct18.n.log -0.021557732
## D.T.or                                       D.T.or -0.022614420
## D.chrs.pnct10.n.log             D.chrs.pnct10.n.log -0.024107007
## D.T.from                                   D.T.from -0.030653359
## D.T.light                                 D.T.light -0.032814746
## D.T.condit                               D.T.condit -0.033248924
## D.chrs.pnct08.n.log             D.chrs.pnct08.n.log -0.039606364
## D.T.minor                                 D.T.minor -0.042692360
## startprice.dgt3.is9             startprice.dgt3.is9 -0.043150483
## D.chrs.pnct12.n.log             D.chrs.pnct12.n.log -0.049684867
## D.chrs.pnct28.n.log             D.chrs.pnct28.n.log -0.052538378
## D.T.mint                                   D.T.mint -0.055691207
## D.chrs.pnct09.n.log             D.chrs.pnct09.n.log -0.061919863
## D.T.with                                   D.T.with -0.066087601
## D.T.in.                                     D.T.in. -0.071653919
## D.chrs.pnct14.n.log             D.chrs.pnct14.n.log -0.075911474
## D.dgts.n.log                           D.dgts.n.log -0.082741631
## D.T.cosmet                               D.T.cosmet -0.088480863
## D.chrs.pnct06.n.log             D.chrs.pnct06.n.log -0.095888068
## D.T.X100                                   D.T.X100 -0.111398502
## D.chrs.pnct05.n.log             D.chrs.pnct05.n.log -0.120173065
## D.T.hous                                   D.T.hous -0.129274878
## D.P.http                                   D.P.http           NA
## D.chrs.pnct02.n.log             D.chrs.pnct02.n.log           NA
## D.chrs.pnct04.n.log             D.chrs.pnct04.n.log           NA
## D.chrs.pnct19.n.log             D.chrs.pnct19.n.log           NA
## D.chrs.pnct20.n.log             D.chrs.pnct20.n.log           NA
## D.chrs.pnct21.n.log             D.chrs.pnct21.n.log           NA
## D.chrs.pnct22.n.log             D.chrs.pnct22.n.log           NA
## D.chrs.pnct23.n.log             D.chrs.pnct23.n.log           NA
## D.chrs.pnct24.n.log             D.chrs.pnct24.n.log           NA
## D.chrs.pnct25.n.log             D.chrs.pnct25.n.log           NA
## D.chrs.pnct26.n.log             D.chrs.pnct26.n.log           NA
## D.chrs.pnct27.n.log             D.chrs.pnct27.n.log           NA
## D.chrs.pnct29.n.log             D.chrs.pnct29.n.log           NA
## D.chrs.pnct30.n.log             D.chrs.pnct30.n.log           NA
##                           exclude.as.feat   cor.y.abs          cor.high.X
## D.T.of                                  0 0.052259530                <NA>
## D.chrs.pnct15.n.log                     0 0.048611857                <NA>
## D.terms.n.stem.stop.Ratio               0 0.043562113                <NA>
## D.T.screen                              0 0.040605274                <NA>
## D.chrs.pnct17.n.log                     0 0.025087653                <NA>
## D.chrs.pnct07.n.log                     0 0.024330390                <NA>
## D.P.white                               0 0.018597281                <NA>
## D.T.good                                0 0.016394675                <NA>
## D.chrs.pnct16.n.log                     0 0.015642041                <NA>
## D.chrs.pnct01.n.log                     0 0.004291712                <NA>
## D.P.spacegray                           0 0.003532788                <NA>
## D.T.use                                 0 0.003025811                <NA>
## D.P.black                               0 0.001181544                <NA>
## D.P.mini                                0 0.006894908                <NA>
## D.P.air                                 0 0.009203749                <NA>
## D.P.gold                                0 0.021557732                <NA>
## D.chrs.pnct03.n.log                     0 0.021557732                <NA>
## D.chrs.pnct18.n.log                     0 0.021557732                <NA>
## D.T.or                                  0 0.022614420                <NA>
## D.chrs.pnct10.n.log                     0 0.024107007                <NA>
## D.T.from                                0 0.030653359                <NA>
## D.T.light                               0 0.032814746                <NA>
## D.T.condit                              0 0.033248924                <NA>
## D.chrs.pnct08.n.log                     0 0.039606364                <NA>
## D.T.minor                               0 0.042692360                <NA>
## startprice.dgt3.is9                     0 0.043150483                <NA>
## D.chrs.pnct12.n.log                     0 0.049684867                <NA>
## D.chrs.pnct28.n.log                     0 0.052538378                <NA>
## D.T.mint                                0 0.055691207                <NA>
## D.chrs.pnct09.n.log                     0 0.061919863                <NA>
## D.T.with                                0 0.066087601                <NA>
## D.T.in.                                 0 0.071653919                <NA>
## D.chrs.pnct14.n.log                     0 0.075911474                <NA>
## D.dgts.n.log                            0 0.082741631                <NA>
## D.T.cosmet                              0 0.088480863                <NA>
## D.chrs.pnct06.n.log                     0 0.095888068                <NA>
## D.T.X100                                0 0.111398502 D.chrs.pnct05.n.log
## D.chrs.pnct05.n.log                     0 0.120173065                <NA>
## D.T.hous                                0 0.129274878                <NA>
## D.P.http                                0          NA                <NA>
## D.chrs.pnct02.n.log                     0          NA                <NA>
## D.chrs.pnct04.n.log                     0          NA                <NA>
## D.chrs.pnct19.n.log                     0          NA                <NA>
## D.chrs.pnct20.n.log                     0          NA                <NA>
## D.chrs.pnct21.n.log                     0          NA                <NA>
## D.chrs.pnct22.n.log                     0          NA                <NA>
## D.chrs.pnct23.n.log                     0          NA                <NA>
## D.chrs.pnct24.n.log                     0          NA                <NA>
## D.chrs.pnct25.n.log                     0          NA                <NA>
## D.chrs.pnct26.n.log                     0          NA                <NA>
## D.chrs.pnct27.n.log                     0          NA                <NA>
## D.chrs.pnct29.n.log                     0          NA                <NA>
## D.chrs.pnct30.n.log                     0          NA                <NA>
##                            freqRatio percentUnique zeroVar  nzv
## D.T.of                     145.00000    1.13513514   FALSE TRUE
## D.chrs.pnct15.n.log        152.66667    0.16216216   FALSE TRUE
## D.terms.n.stem.stop.Ratio   85.14286    0.64864865   FALSE TRUE
## D.T.screen                  70.91667    1.13513514   FALSE TRUE
## D.chrs.pnct17.n.log       1849.00000    0.10810811   FALSE TRUE
## D.chrs.pnct07.n.log        107.52941    0.16216216   FALSE TRUE
## D.P.white                  230.12500    0.16216216   FALSE TRUE
## D.T.good                    95.16667    1.18918919   FALSE TRUE
## D.chrs.pnct16.n.log        263.14286    0.16216216   FALSE TRUE
## D.chrs.pnct01.n.log         52.70588    0.32432432   FALSE TRUE
## D.P.spacegray              461.50000    0.10810811   FALSE TRUE
## D.T.use                     86.63158    1.51351351   FALSE TRUE
## D.P.black                  167.18182    0.10810811   FALSE TRUE
## D.P.mini                    96.31579    0.16216216   FALSE TRUE
## D.P.air                    122.26667    0.16216216   FALSE TRUE
## D.P.gold                  1849.00000    0.10810811   FALSE TRUE
## D.chrs.pnct03.n.log       1849.00000    0.10810811   FALSE TRUE
## D.chrs.pnct18.n.log       1849.00000    0.10810811   FALSE TRUE
## D.T.or                      87.95000    1.08108108   FALSE TRUE
## D.chrs.pnct10.n.log        307.16667    0.16216216   FALSE TRUE
## D.T.from                    95.31579    0.70270270   FALSE TRUE
## D.T.light                   99.27778    0.91891892   FALSE TRUE
## D.T.condit                  36.82927    1.35135135   FALSE TRUE
## D.chrs.pnct08.n.log         69.23077    0.21621622   FALSE TRUE
## D.T.minor                   92.52632    0.97297297   FALSE TRUE
## startprice.dgt3.is9        461.50000    0.10810811   FALSE TRUE
## D.chrs.pnct12.n.log         28.46774    0.21621622   FALSE TRUE
## D.chrs.pnct28.n.log        461.00000    0.16216216   FALSE TRUE
## D.T.mint                    94.89474    1.02702703   FALSE TRUE
## D.chrs.pnct09.n.log        306.83333    0.21621622   FALSE TRUE
## D.T.with                    46.88889    1.02702703   FALSE TRUE
## D.T.in.                     33.52174    1.35135135   FALSE TRUE
## D.chrs.pnct14.n.log         35.17647    0.27027027   FALSE TRUE
## D.dgts.n.log                34.34000    0.59459459   FALSE TRUE
## D.T.cosmet                  88.40000    0.81081081   FALSE TRUE
## D.chrs.pnct06.n.log         60.46667    0.16216216   FALSE TRUE
## D.T.X100                   180.50000    0.64864865   FALSE TRUE
## D.chrs.pnct05.n.log         39.21739    0.10810811   FALSE TRUE
## D.T.hous                    81.68182    0.54054054   FALSE TRUE
## D.P.http                     0.00000    0.05405405    TRUE TRUE
## D.chrs.pnct02.n.log          0.00000    0.05405405    TRUE TRUE
## D.chrs.pnct04.n.log          0.00000    0.05405405    TRUE TRUE
## D.chrs.pnct19.n.log          0.00000    0.05405405    TRUE TRUE
## D.chrs.pnct20.n.log          0.00000    0.05405405    TRUE TRUE
## D.chrs.pnct21.n.log          0.00000    0.05405405    TRUE TRUE
## D.chrs.pnct22.n.log          0.00000    0.05405405    TRUE TRUE
## D.chrs.pnct23.n.log          0.00000    0.05405405    TRUE TRUE
## D.chrs.pnct24.n.log          0.00000    0.05405405    TRUE TRUE
## D.chrs.pnct25.n.log          0.00000    0.05405405    TRUE TRUE
## D.chrs.pnct26.n.log          0.00000    0.05405405    TRUE TRUE
## D.chrs.pnct27.n.log          0.00000    0.05405405    TRUE TRUE
## D.chrs.pnct29.n.log          0.00000    0.05405405    TRUE TRUE
## D.chrs.pnct30.n.log          0.00000    0.05405405    TRUE TRUE
##                           is.cor.y.abs.low
## D.T.of                               FALSE
## D.chrs.pnct15.n.log                  FALSE
## D.terms.n.stem.stop.Ratio            FALSE
## D.T.screen                           FALSE
## D.chrs.pnct17.n.log                  FALSE
## D.chrs.pnct07.n.log                  FALSE
## D.P.white                            FALSE
## D.T.good                             FALSE
## D.chrs.pnct16.n.log                  FALSE
## D.chrs.pnct01.n.log                  FALSE
## D.P.spacegray                        FALSE
## D.T.use                              FALSE
## D.P.black                             TRUE
## D.P.mini                             FALSE
## D.P.air                              FALSE
## D.P.gold                             FALSE
## D.chrs.pnct03.n.log                  FALSE
## D.chrs.pnct18.n.log                  FALSE
## D.T.or                               FALSE
## D.chrs.pnct10.n.log                  FALSE
## D.T.from                             FALSE
## D.T.light                            FALSE
## D.T.condit                           FALSE
## D.chrs.pnct08.n.log                  FALSE
## D.T.minor                            FALSE
## startprice.dgt3.is9                  FALSE
## D.chrs.pnct12.n.log                  FALSE
## D.chrs.pnct28.n.log                  FALSE
## D.T.mint                             FALSE
## D.chrs.pnct09.n.log                  FALSE
## D.T.with                             FALSE
## D.T.in.                              FALSE
## D.chrs.pnct14.n.log                  FALSE
## D.dgts.n.log                         FALSE
## D.T.cosmet                           FALSE
## D.chrs.pnct06.n.log                  FALSE
## D.T.X100                             FALSE
## D.chrs.pnct05.n.log                  FALSE
## D.T.hous                             FALSE
## D.P.http                                NA
## D.chrs.pnct02.n.log                     NA
## D.chrs.pnct04.n.log                     NA
## D.chrs.pnct19.n.log                     NA
## D.chrs.pnct20.n.log                     NA
## D.chrs.pnct21.n.log                     NA
## D.chrs.pnct22.n.log                     NA
## D.chrs.pnct23.n.log                     NA
## D.chrs.pnct24.n.log                     NA
## D.chrs.pnct25.n.log                     NA
## D.chrs.pnct26.n.log                     NA
## D.chrs.pnct27.n.log                     NA
## D.chrs.pnct29.n.log                     NA
## D.chrs.pnct30.n.log                     NA
tmp_allobs_df <- 
    glb_allobs_df[, union(setdiff(names(glb_allobs_df), subset(glb_feats_df, nzv)$id),
                          glb_cluster_entropy_var)]
glb_trnobs_df <- subset(tmp_allobs_df, .src == "Train")
glb_newobs_df <- subset(tmp_allobs_df, .src == "Test")

glb_feats_df$interaction.feat <- NA
for (feat in names(glb_interaction_only_feats_lst))
    glb_feats_df[glb_feats_df$id %in% feat, "interaction.feat"] <-
        glb_interaction_only_feats_lst[[feat]]
        
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
indep_vars <- subset(glb_feats_df, !nzv & (exclude.as.feat != 1))[, "id"]
numeric_indep_vars <- indep_vars[!grepl(".fctr", indep_vars, fixed=TRUE)]
glb_feats_df$shapiro.test.p.value <- NA
glb_feats_df[glb_feats_df$id %in% numeric_indep_vars, "shapiro.test.p.value"] <- 
    sapply(numeric_indep_vars, function(var) shapiro.test(glb_trnobs_df[, var])$p.value)
not_nrml_feats_df <- glb_feats_df %>%
                        subset(!is.na(shapiro.test.p.value)) %>%
                        subset((shapiro.test.p.value < 0.05) || (id == ".rnorm")) %>%
                        arrange(shapiro.test.p.value)
row.names(not_nrml_feats_df) <- not_nrml_feats_df$id

#plt_trnobs_df <- glb_trnobs_df[, c("D.npnct05.log", ".rnorm")]
plt_trnobs_df <- glb_trnobs_df[, c(union(not_nrml_feats_df$id[1:min(5, nrow(not_nrml_feats_df))],
                                   ".rnorm"), glb_cluster_entropy_var)]
print(myplot_violin(plt_trnobs_df, setdiff(names(plt_trnobs_df), glb_cluster_entropy_var), 
                    xcol_name = glb_cluster_entropy_var) +
          facet_wrap(~variable, scales="free"))

#myplot_histogram(plt_trnobs_df, "D.npnct11.log", fill_col_name="sold", show_stats = TRUE)

myadjust_interaction_feats <- function(vars_vctr) {
    for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
        if (feat %in% vars_vctr)
            vars_vctr <- union(setdiff(vars_vctr, feat), 
                paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":",
                       feat))
    return(vars_vctr)
}

myrun_rfe <- function(obs_df, indep_vars, sizes = NULL) {
    rfe_obs_df <- myget_vectorized_obs_df(obs_df, glb_rsp_var, indep_vars)
    predictors_vctr <- setdiff(names(rfe_obs_df), glb_rsp_var)
    
    if (glb_is_regression)  rfeFuncs <- lmFuncs else {    
        rfeFuncs <- ldaFuncs
        
        # Delete non-variant columns
        predictors_unqLen <- sapply(predictors_vctr, function(col)
                                                length(unique(rfe_obs_df[, col])))
        predictors_vctr <- predictors_vctr[predictors_unqLen > 1]
        # Delete freqRatio >= 291
        #   plagiarized from caret:::nzv
        predictors_freqRatio <- 
            apply(rfe_obs_df[, predictors_vctr], 2, function(data) {
                t <- table(data[!is.na(data)])
                if (length(t) <= 1) {
                    return(0)
                }
                w <- which.max(t)
                return(max(t, na.rm = TRUE)/max(t[-w], na.rm = TRUE))
            })
        predictors_vctr <- predictors_vctr[predictors_freqRatio < 172]
    }                        
    
    if (is.null(sizes))
        sizes <- tail(2 ^ (1:as.integer(log2(length(predictors_vctr)))), 5)
    
    rfe_control <- rfeControl(functions = rfeFuncs, method = "repeatedcv",
                              number = glb_rcv_n_folds, repeats = glb_rcv_n_repeats,
                              verbose = TRUE, returnResamp = "all",
        seeds = mygen_seeds(seeds_lst_len = 
                                (glb_rcv_n_folds * glb_rcv_n_repeats) + 1,
                            seeds_elmnt_lst_len = (length(sizes) + 1))
                            , allowParallel = FALSE
                            )
    set.seed(113)
    rfe_results <- rfe(rfe_obs_df[, predictors_vctr], 
                       rfe_obs_df[, glb_rsp_var],
                       sizes = sizes, 
                       # metric = unlist(strsplit(glbMdlMetricsEval, "[.]"))[2],
#         maximize = ifelse(unlist(strsplit(glbMdlMetricsEval, "[.]"))[1] == "max",
#                                        TRUE, FALSE),
                       rfeControl = rfe_control)
    print(rfe_results)
    print(predictors(rfe_results))
    # print(plot(rfe_results, type=c("g", "o")))
    # print(plot(rfe_results))
    print(ggplot(rfe_results))

    return(rfe_results)
}

#stop(here"); glb_to_sav()
# shd .clusterid.fctr be excluded from this ? or include encoding of glb_category_var:.clusterid.fctr ?
indep_vars <- myadjust_interaction_feats(subset(glb_feats_df, 
                                                !nzv & (exclude.as.feat != 1))$id)
rfe_fit_results <- myrun_rfe(obs_df = glb_fitobs_df, indep_vars = indep_vars, 
                             sizes = glb_rfe_fit_sizes)
## +(rfe) fit Fold1.Rep1 size: 70
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold1.Rep1 size: 70 
## +(rfe) imp Fold1.Rep1 
## -(rfe) imp Fold1.Rep1 
## +(rfe) fit Fold1.Rep1 size: 64
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold1.Rep1 size: 64 
## +(rfe) fit Fold1.Rep1 size: 32 
## -(rfe) fit Fold1.Rep1 size: 32 
## +(rfe) fit Fold1.Rep1 size: 16 
## -(rfe) fit Fold1.Rep1 size: 16 
## +(rfe) fit Fold1.Rep1 size:  8 
## -(rfe) fit Fold1.Rep1 size:  8 
## +(rfe) fit Fold1.Rep1 size:  4 
## -(rfe) fit Fold1.Rep1 size:  4 
## +(rfe) fit Fold2.Rep1 size: 70
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold2.Rep1 size: 70 
## +(rfe) imp Fold2.Rep1 
## -(rfe) imp Fold2.Rep1 
## +(rfe) fit Fold2.Rep1 size: 64
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold2.Rep1 size: 64 
## +(rfe) fit Fold2.Rep1 size: 32 
## -(rfe) fit Fold2.Rep1 size: 32 
## +(rfe) fit Fold2.Rep1 size: 16 
## -(rfe) fit Fold2.Rep1 size: 16 
## +(rfe) fit Fold2.Rep1 size:  8 
## -(rfe) fit Fold2.Rep1 size:  8 
## +(rfe) fit Fold2.Rep1 size:  4 
## -(rfe) fit Fold2.Rep1 size:  4 
## +(rfe) fit Fold3.Rep1 size: 70
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold3.Rep1 size: 70 
## +(rfe) imp Fold3.Rep1 
## -(rfe) imp Fold3.Rep1 
## +(rfe) fit Fold3.Rep1 size: 64
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold3.Rep1 size: 64 
## +(rfe) fit Fold3.Rep1 size: 32 
## -(rfe) fit Fold3.Rep1 size: 32 
## +(rfe) fit Fold3.Rep1 size: 16 
## -(rfe) fit Fold3.Rep1 size: 16 
## +(rfe) fit Fold3.Rep1 size:  8 
## -(rfe) fit Fold3.Rep1 size:  8 
## +(rfe) fit Fold3.Rep1 size:  4 
## -(rfe) fit Fold3.Rep1 size:  4 
## +(rfe) fit Fold1.Rep2 size: 70
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold1.Rep2 size: 70 
## +(rfe) imp Fold1.Rep2 
## -(rfe) imp Fold1.Rep2 
## +(rfe) fit Fold1.Rep2 size: 64
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold1.Rep2 size: 64 
## +(rfe) fit Fold1.Rep2 size: 32 
## -(rfe) fit Fold1.Rep2 size: 32 
## +(rfe) fit Fold1.Rep2 size: 16 
## -(rfe) fit Fold1.Rep2 size: 16 
## +(rfe) fit Fold1.Rep2 size:  8 
## -(rfe) fit Fold1.Rep2 size:  8 
## +(rfe) fit Fold1.Rep2 size:  4 
## -(rfe) fit Fold1.Rep2 size:  4 
## +(rfe) fit Fold2.Rep2 size: 70
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold2.Rep2 size: 70 
## +(rfe) imp Fold2.Rep2 
## -(rfe) imp Fold2.Rep2 
## +(rfe) fit Fold2.Rep2 size: 64
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold2.Rep2 size: 64 
## +(rfe) fit Fold2.Rep2 size: 32 
## -(rfe) fit Fold2.Rep2 size: 32 
## +(rfe) fit Fold2.Rep2 size: 16 
## -(rfe) fit Fold2.Rep2 size: 16 
## +(rfe) fit Fold2.Rep2 size:  8 
## -(rfe) fit Fold2.Rep2 size:  8 
## +(rfe) fit Fold2.Rep2 size:  4 
## -(rfe) fit Fold2.Rep2 size:  4 
## +(rfe) fit Fold3.Rep2 size: 70
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold3.Rep2 size: 70 
## +(rfe) imp Fold3.Rep2 
## -(rfe) imp Fold3.Rep2 
## +(rfe) fit Fold3.Rep2 size: 64
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold3.Rep2 size: 64 
## +(rfe) fit Fold3.Rep2 size: 32 
## -(rfe) fit Fold3.Rep2 size: 32 
## +(rfe) fit Fold3.Rep2 size: 16 
## -(rfe) fit Fold3.Rep2 size: 16 
## +(rfe) fit Fold3.Rep2 size:  8 
## -(rfe) fit Fold3.Rep2 size:  8 
## +(rfe) fit Fold3.Rep2 size:  4 
## -(rfe) fit Fold3.Rep2 size:  4 
## +(rfe) fit Fold1.Rep3 size: 70
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold1.Rep3 size: 70 
## +(rfe) imp Fold1.Rep3 
## -(rfe) imp Fold1.Rep3 
## +(rfe) fit Fold1.Rep3 size: 64
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold1.Rep3 size: 64 
## +(rfe) fit Fold1.Rep3 size: 32 
## -(rfe) fit Fold1.Rep3 size: 32 
## +(rfe) fit Fold1.Rep3 size: 16 
## -(rfe) fit Fold1.Rep3 size: 16 
## +(rfe) fit Fold1.Rep3 size:  8 
## -(rfe) fit Fold1.Rep3 size:  8 
## +(rfe) fit Fold1.Rep3 size:  4 
## -(rfe) fit Fold1.Rep3 size:  4 
## +(rfe) fit Fold2.Rep3 size: 70
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold2.Rep3 size: 70 
## +(rfe) imp Fold2.Rep3 
## -(rfe) imp Fold2.Rep3 
## +(rfe) fit Fold2.Rep3 size: 64
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold2.Rep3 size: 64 
## +(rfe) fit Fold2.Rep3 size: 32 
## -(rfe) fit Fold2.Rep3 size: 32 
## +(rfe) fit Fold2.Rep3 size: 16 
## -(rfe) fit Fold2.Rep3 size: 16 
## +(rfe) fit Fold2.Rep3 size:  8 
## -(rfe) fit Fold2.Rep3 size:  8 
## +(rfe) fit Fold2.Rep3 size:  4 
## -(rfe) fit Fold2.Rep3 size:  4 
## +(rfe) fit Fold3.Rep3 size: 70
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold3.Rep3 size: 70 
## +(rfe) imp Fold3.Rep3 
## -(rfe) imp Fold3.Rep3 
## +(rfe) fit Fold3.Rep3 size: 64
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold3.Rep3 size: 64 
## +(rfe) fit Fold3.Rep3 size: 32 
## -(rfe) fit Fold3.Rep3 size: 32 
## +(rfe) fit Fold3.Rep3 size: 16 
## -(rfe) fit Fold3.Rep3 size: 16 
## +(rfe) fit Fold3.Rep3 size:  8 
## -(rfe) fit Fold3.Rep3 size:  8 
## +(rfe) fit Fold3.Rep3 size:  4 
## -(rfe) fit Fold3.Rep3 size:  4
## Warning in lda.default(x, grouping, ...): variables are collinear
## 
## Recursive feature selection
## 
## Outer resampling method: Cross-Validated (3 fold, repeated 3 times) 
## 
## Resampling performance over subset size:
## 
##  Variables Accuracy  Kappa AccuracySD KappaSD Selected
##          4   0.7951 0.5866    0.01779 0.03553         
##          8   0.7954 0.5870    0.02906 0.05706         
##         16   0.8046 0.6049    0.02248 0.04531         
##         32   0.8118 0.6190    0.02555 0.05141         
##         64   0.8183 0.6330    0.02513 0.05031         
##         70   0.8258 0.6482    0.02137 0.04275        *
## 
## The top 5 variables (out of 70):
##    sprice.d20nexp, sprice.log10, sprice.root2, biddable, spdiff.cut.fctr(1,10]
## 
##  [1] "sprice.d20nexp"                          
##  [2] "sprice.log10"                            
##  [3] "sprice.root2"                            
##  [4] "biddable"                                
##  [5] "spdiff.cut.fctr(1,10]"                   
##  [6] "spdiff.cut.fctr(-1,0]"                   
##  [7] "spdiff.cut.fctr(10,100]"                 
##  [8] "cellular.fctr0:carrier.fctrNone"         
##  [9] "spdiff.cut.fctr(-10,-1]"                 
## [10] "prdl.my.fctriPad1"                       
## [11] "spdiff.cut.fctr(0,1]"                    
## [12] "D.ratio.wrds.stop.n.wrds.n"              
## [13] "storage.fctr16"                          
## [14] "condition.fctrFor parts or not working"  
## [15] "D.weight.sum.stem.stop.Ratio"            
## [16] "color.fctrUnknown"                       
## [17] "prdl.my.fctriPad3"                       
## [18] "startprice.dcm2.is9"                     
## [19] "prdl.my.fctriPad2"                       
## [20] ".rnorm"                                  
## [21] "storage.fctr32"                          
## [22] "prdl.my.fctriPadmini"                    
## [23] "cellular.fctr1:carrier.fctrVerizon"      
## [24] "D.chrs.pnct11.n.log"                     
## [25] "prdl.my.fctriPad1:.clusterid.fctr2"      
## [26] "prdl.my.fctriPadAir:.clusterid.fctr2"    
## [27] "prdl.my.fctriPadmini2"                   
## [28] "prdl.my.fctriPad1:.clusterid.fctr3"      
## [29] "prdl.my.fctriPad4:.clusterid.fctr2"      
## [30] "prdl.my.fctriPad3:.clusterid.fctr2"      
## [31] "cellular.fctr1:carrier.fctrSprint"       
## [32] "storage.fctr64"                          
## [33] "prdl.my.fctrUnknown:.clusterid.fctr3"    
## [34] "D.wrds.stop.n.log"                       
## [35] "cellular.fctr1:carrier.fctrT-Mobile"     
## [36] "prdl.my.fctrUnknown:.clusterid.fctr2"    
## [37] "condition.fctrManufacturer refurbished"  
## [38] "startprice.dcm1.is9"                     
## [39] "condition.fctrNew other (see details)"   
## [40] "prdl.my.fctriPadAir"                     
## [41] "prdl.my.fctriPad4"                       
## [42] "color.fctrGold"                          
## [43] "cellular.fctr1:carrier.fctrUnknown"      
## [44] "prdl.my.fctriPad4:.clusterid.fctr3"      
## [45] "startprice.dgt2.is9"                     
## [46] "color.fctrWhite"                         
## [47] "prdl.my.fctriPadAir2"                    
## [48] "D.ratio.weight.sum.wrds.n"               
## [49] "storage.fctrUnknown"                     
## [50] "color.fctrSpace Gray"                    
## [51] "spdiff.cut.fctr(100,1e+03]"              
## [52] "D.chrs.pnct13.n.log"                     
## [53] "cellular.fctrUnknown"                    
## [54] "cellular.fctrUnknown:carrier.fctrUnknown"
## [55] "prdl.my.fctriPadmini3"                   
## [56] "cellular.fctr1"                          
## [57] "condition.fctrSeller refurbished"        
## [58] "D.weight.post.stop.sum"                  
## [59] "D.weight.post.stem.sum"                  
## [60] "D.weight.sum"                            
## [61] "D.wrds.n.log"                            
## [62] "D.chrs.uppr.n.log"                       
## [63] "D.wrds.unq.n.log"                        
## [64] "D.terms.post.stop.n.log"                 
## [65] "D.terms.post.stem.n.log"                 
## [66] "D.chrs.n.log"                            
## [67] "condition.fctrNew"                       
## [68] "startprice.dgt1.is9"                     
## [69] "spdiff.cut.fctr(-100,-10]"               
## [70] "spdiff.cut.fctr(-1e+03,-100]"

# print(all.equal(rfe_results[-which(names(rfe_results) == "times")], 
#                 sav_rfe_results[-which(names(sav_rfe_results) == "times")]))

# require(mRMRe)
# indep_vars_vctr <- subset(glb_feats_df, !nzv &
#                                         (exclude.as.feat != 1))[, "id"]
# indep_vars_vctr <- setdiff(indep_vars_vctr, 
#                     myfind_fctr_cols_df(glb_trnobs_df[, c(glb_rsp_var, indep_vars_vctr)]))
# tmp_trnobs_df <- glb_trnobs_df[, c(glb_rsp_var, indep_vars_vctr)]
# tmp_trnobs_df$biddable <- as.numeric(tmp_trnobs_df$biddable)
# dd <- mRMR.data(data = tmp_trnobs_df)
# mRMRe.fltr <- mRMR.classic(data = dd, target_indices = c(1), feature_count = 10)
# print(solutions(mRMRe.fltr)[[1]])
# print(apply(solutions(mRMRe.fltr)[[1]], 2, function(x, y) { return(y[x]) },
#             y=featureNames(dd)))
# print(featureNames(dd)[solutions(mRMRe.fltr)[[1]]])
# print(mRMRe.fltr@filters); print(mRMRe.fltr@scores)

mycheck_problem_data(glb_allobs_df, featsExclude = glbFeatsExclude, 
                     fctrMaxUniqVals = glbFctrMaxUniqVals, terminate = TRUE)
## [1] "numeric data missing in : "
##      sold sold.fctr 
##       798       798 
## [1] "numeric data w/ 0s in : "
##                  biddable                      sold 
##                      1437                       995 
##              sprice.log10             cellular.fctr 
##                        31                      1590 
##       D.terms.post.stop.n   D.terms.post.stop.n.log 
##                      1516                      1516 
##    D.weight.post.stop.sum       D.terms.post.stem.n 
##                      1516                      1516 
##   D.terms.post.stem.n.log    D.weight.post.stem.sum 
##                      1516                      1516 
##                D.T.condit                   D.T.use 
##                      2156                      2363 
##                   D.T.in.                  D.T.good 
##                      2216                      2451 
##                D.T.screen                  D.T.with 
##                      2440                      2434 
##                    D.T.of                  D.T.mint 
##                      2497                      2585 
##                    D.T.or                D.T.cosmet 
##                      2523                      2531 
##                 D.T.minor                 D.T.light 
##                      2531                      2567 
##                  D.T.X100                  D.T.from 
##                      2584                      2591 
##                  D.T.hous              D.wrds.n.log 
##                      2576                      1513 
##          D.wrds.unq.n.log              D.weight.sum 
##                      1516                      1516 
## D.ratio.weight.sum.wrds.n              D.chrs.n.log 
##                      1516                      1513 
##         D.chrs.uppr.n.log              D.dgts.n.log 
##                      1515                      2452 
##       D.chrs.pnct01.n.log       D.chrs.pnct02.n.log 
##                      2570                      2648 
##       D.chrs.pnct03.n.log       D.chrs.pnct04.n.log 
##                      2647                      2648 
##       D.chrs.pnct05.n.log       D.chrs.pnct06.n.log 
##                      2582                      2596 
##       D.chrs.pnct07.n.log       D.chrs.pnct08.n.log 
##                      2611                      2572 
##       D.chrs.pnct09.n.log       D.chrs.pnct10.n.log 
##                      2632                      2639 
##       D.chrs.pnct11.n.log       D.chrs.pnct12.n.log 
##                      2294                      2534 
##       D.chrs.pnct13.n.log       D.chrs.pnct14.n.log 
##                      1930                      2574 
##       D.chrs.pnct15.n.log       D.chrs.pnct16.n.log 
##                      2628                      2639 
##       D.chrs.pnct17.n.log       D.chrs.pnct18.n.log 
##                      2646                      2647 
##       D.chrs.pnct19.n.log       D.chrs.pnct20.n.log 
##                      2648                      2648 
##       D.chrs.pnct21.n.log       D.chrs.pnct22.n.log 
##                      2648                      2648 
##       D.chrs.pnct23.n.log       D.chrs.pnct24.n.log 
##                      2648                      2648 
##       D.chrs.pnct25.n.log       D.chrs.pnct26.n.log 
##                      2648                      2648 
##       D.chrs.pnct27.n.log       D.chrs.pnct28.n.log 
##                      2648                      2640 
##       D.chrs.pnct29.n.log       D.chrs.pnct30.n.log 
##                      2648                      2648 
##         D.wrds.stop.n.log                  D.P.http 
##                      1965                      2648 
##                  D.P.mini                   D.P.air 
##                      2615                      2627 
##                 D.P.black                 D.P.white 
##                      2631                      2638 
##                  D.P.gold             D.P.spacegray 
##                      2647                      2642 
##       startprice.dgt1.is9       startprice.dgt2.is9 
##                      1779                      2290 
##       startprice.dgt3.is9       startprice.dcm1.is9 
##                      2643                      1653 
##       startprice.dcm2.is9 
##                      1826 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description   condition    cellular     carrier       color     storage 
##        1513           0           0           0           0           0 
## productline      .grpid    descr.my        .lcn 
##           0          NA        1513         798
# glb_allobs_df %>% filter(is.na(Married.fctr)) %>% tbl_df()
# glb_allobs_df %>% count(Married.fctr)
# levels(glb_allobs_df$Married.fctr)

print("glb_feats_df:");   print(dim(glb_feats_df))
## [1] "glb_feats_df:"
## [1] 93 12
sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df

glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE 
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_var) && glb_id_var != ".rownames")
    glb_feats_df[glb_feats_df$id %in% glb_id_var, "id_var"] <- TRUE 
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)
##                  id exclude.as.feat rsp_var
## sold.fctr sold.fctr            TRUE    TRUE
glb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
if (glb_id_var != ".rownames")
    print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var)) else
    print(subset(glb_feats_df, rsp_var_raw | rsp_var))    
##                  id      cor.y exclude.as.feat cor.y.abs cor.high.X
## sold           sold  1.0000000            TRUE 1.0000000       <NA>
## UniqueID   UniqueID -0.1906261            TRUE 0.1906261       <NA>
## sold.fctr sold.fctr         NA            TRUE        NA       <NA>
##           freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## sold       1.163743     0.1081081   FALSE FALSE            FALSE
## UniqueID   1.000000   100.0000000   FALSE FALSE            FALSE
## sold.fctr        NA            NA      NA    NA               NA
##           interaction.feat shapiro.test.p.value rsp_var_raw id_var rsp_var
## sold                  <NA>                   NA        TRUE     NA      NA
## UniqueID              <NA>                   NA       FALSE   TRUE      NA
## sold.fctr             <NA>                   NA          NA     NA    TRUE
print("glb_feats_df vs. glb_allobs_df: "); 
## [1] "glb_feats_df vs. glb_allobs_df: "
print(setdiff(glb_feats_df$id, names(glb_allobs_df)))
## character(0)
print("glb_allobs_df vs. glb_feats_df: "); 
## [1] "glb_allobs_df vs. glb_feats_df: "
# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id), 
                myfind_chr_cols_df(glb_allobs_df)))
## character(0)
if (glb_save_envir)
    save(glb_feats_df, 
         glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
         file=paste0(glb_out_pfx, "selfts_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))

# if (!all.equal(tmp_feats_df, glb_feats_df))
#     stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
#     stop("glb_allobs_df r/w not working")

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)
##              label step_major step_minor label_minor     bgn     end
## 9  select.features          5          0           0 331.640 342.849
## 10      fit.models          6          0           0 342.849      NA
##    elapsed
## 9   11.209
## 10      NA

Step 6.0: fit models

# load(paste0(glb_out_pfx, "dsk.RData"))

get_model_sel_frmla <- function() {
    model_evl_terms <- c(NULL)
    # min.aic.fit might not be avl
    lclMdlEvlCriteria <- 
        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
    for (metric in lclMdlEvlCriteria)
        model_evl_terms <- c(model_evl_terms, 
                             ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
    if (glb_is_classification && glb_is_binomial)
        model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
    model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
    return(model_sel_frmla)
}

get_dsp_models_df <- function() {
    dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    dsp_models_df <- 
        #orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glbMdlMetricsEval)]
        orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols]    
    nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
    nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0, 
        nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
    
#     nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
#     nParams <- nParams[names(nParams) != "avNNet"]    
    
    if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
        print("Cross Validation issues:")
        warning("Cross Validation issues:")        
        print(cvMdlProblems)
    }
    
    pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
    pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
    
    # length(pltMdls) == 21
    png(paste0(glb_out_pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
    pltIx <- 1
    for (mdlId in pltMdls) {
        print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),   
              vp = viewport(layout.pos.row = ceiling(pltIx / 2.0), 
                            layout.pos.col = ((pltIx - 1) %% 2) + 1))  
        pltIx <- pltIx + 1
    }
    dev.off()

    return(dsp_models_df)
}    
#get_dsp_models_df()

if (glb_is_classification && glb_is_binomial && 
        (length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
    stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
         paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))

max_cor_y_x_vars <- orderBy(~ -cor.y.abs, 
        subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low & 
                                is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
#     max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs, 
#             subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])    
# }

#stop(here"); glb_to_sav(); glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
if (!is.null(glb_Baseline_mdl_var)) {
    if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) & 
        (glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] > 
         glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
        stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var, 
             " than the Baseline var: ", glb_Baseline_mdl_var)
}

glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
    
# Model specs
c("id.prefix", "method", "type",
  # trainControl params
  "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
  # train params
  "metric", "metric.maximize", "tune.df")
##  [1] "id.prefix"       "method"          "type"           
##  [4] "preProc.method"  "cv.n.folds"      "cv.n.repeats"   
##  [7] "summary.fn"      "metric"          "metric.maximize"
## [10] "tune.df"
# Baseline
if (!is.null(glb_Baseline_mdl_var)) 
    ret_lst <- myfit_mdl(mdl_id="Baseline", 
                         model_method="mybaseln_classfr",
                        indep_vars_vctr=glb_Baseline_mdl_var,
                        rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
                        fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)

# Most Frequent Outcome "MFO" model: mean(y) for regression
#   Not using caret's nullModel since model stats not avl
#   Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
    id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
    train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
                        indep_vars = ".rnorm", rsp_var = glb_rsp_var,
                        fit_df = glb_fitobs_df, OOB_df = glb_OOBobs_df)
## [1] "fitting model: MFO.myMFO_classfr"
## [1] "    indep_vars: .rnorm"
## Warning in if (mdl_specs_lst[["train.method"]] %in% c("bayesglm", "glm", :
## the condition has length > 1 and only the first element will be used
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] N Y
## Levels: N Y
## [1] "unique.prob:"
## y
##         N         Y 
## 0.5372549 0.4627451 
## [1] "MFO.val:"
## [1] "N"
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      -none-     numeric  
## MFO.val     1      -none-     character
## x.names     1      -none-     character
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## Loading required package: ROCR
## Loading required package: gplots
## 
## Attaching package: 'gplots'
## 
## The following object is masked from 'package:wordcloud':
## 
##     textplot
## 
## The following object is masked from 'package:stats':
## 
##     lowess
## [1] "in MFO.Classifier$prob"
##           N         Y
## 1 0.5372549 0.4627451
## 2 0.5372549 0.4627451
## 3 0.5372549 0.4627451
## 4 0.5372549 0.4627451
## 5 0.5372549 0.4627451
## 6 0.5372549 0.4627451

##   sold.fctr sold.fctr.predict.MFO.myMFO_classfr.Y
## 1         N                                   548
## 2         Y                                   472
##          Prediction
## Reference   N   Y
##         N   0 548
##         Y   0 472
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   4.627451e-01   0.000000e+00   4.318011e-01   4.939047e-01   5.372549e-01 
## AccuracyPValue  McnemarPValue 
##   9.999992e-01  9.327417e-121 
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
##           N         Y
## 1 0.5372549 0.4627451
## 2 0.5372549 0.4627451
## 3 0.5372549 0.4627451
## 4 0.5372549 0.4627451
## 5 0.5372549 0.4627451
## 6 0.5372549 0.4627451

##   sold.fctr sold.fctr.predict.MFO.myMFO_classfr.Y
## 1         N                                   447
## 2         Y                                   383
##          Prediction
## Reference   N   Y
##         N   0 447
##         Y   0 383
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   4.614458e-01   0.000000e+00   4.271177e-01   4.960486e-01   5.385542e-01 
## AccuracyPValue  McnemarPValue 
##   9.999963e-01   8.826336e-99 
##                  id  feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO.myMFO_classfr .rnorm               0                      0.292
##   min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1                 0.003             0.5            1            0
##   max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1             0.5                    0.4       0.6327078        0.4627451
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.4318011             0.4939047             0
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1             0.5            1            0             0.5
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.4       0.6314922        0.4614458
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.4271177             0.4960486             0
if (glb_is_classification)
    # "random" model - only for classification; 
    #   none needed for regression since it is same as MFO
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
        train.method = "myrandom_classfr")),
                        indep_vars = ".rnorm", rsp_var = glb_rsp_var,
                        fit_df = glb_fitobs_df, OOB_df = glb_OOBobs_df)
## [1] "fitting model: Random.myrandom_classfr"
## [1] "    indep_vars: .rnorm"
## Warning in if (mdl_specs_lst[["train.method"]] %in% c("bayesglm", "glm", :
## the condition has length > 1 and only the first element will be used
## Fitting parameter = none on full training set
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      table      numeric  
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## [1] "in Random.Classifier$prob"

##   sold.fctr sold.fctr.predict.Random.myrandom_classfr.Y
## 1         N                                         548
## 2         Y                                         472
##          Prediction
## Reference   N   Y
##         N   0 548
##         Y   0 472
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   4.627451e-01   0.000000e+00   4.318011e-01   4.939047e-01   5.372549e-01 
## AccuracyPValue  McnemarPValue 
##   9.999992e-01  9.327417e-121 
## [1] "in Random.Classifier$prob"

##   sold.fctr sold.fctr.predict.Random.myrandom_classfr.Y
## 1         N                                         447
## 2         Y                                         383
##          Prediction
## Reference   N   Y
##         N   0 447
##         Y   0 383
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   4.614458e-01   0.000000e+00   4.271177e-01   4.960486e-01   5.385542e-01 
## AccuracyPValue  McnemarPValue 
##   9.999963e-01   8.826336e-99 
##                        id  feats max.nTuningRuns
## 1 Random.myrandom_classfr .rnorm               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      0.308                 0.001       0.4859659
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.5164234    0.4555085       0.4893217                    0.4
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.6327078        0.4627451             0.4318011
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.4939047             0       0.5064252    0.5480984
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1     0.464752       0.4956046                    0.4       0.6314922
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.4614458             0.4271177             0.4960486
##   max.Kappa.OOB
## 1             0
#     ret_lst <- myfit_mdl(mdl_id = "Random", model_method = "myrandom_classfr",
#                             model_type = glb_model_type,                         
#                             indep_vars_vctr = ".rnorm",
#                             rsp_var = glb_rsp_var, rsp_var_out = glb_rsp_var_out,
#                             fit_df = glb_fitobs_df, OOB_df = glb_OOBobs_df)

# Any models that have tuning parameters has "better" results with cross-validation
#   (except bag & rf) & "different" results for different outcome metrics

# Max.cor.Y
#   Check impact of cv
#       rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
    id.prefix="Max.cor.Y.rcv.1X1", type=glb_model_type, trainControl.method="none",
    train.method="glmnet")),
                    indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var, 
                    fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
## [1] "fitting model: Max.cor.Y.rcv.1X1.glmnet"
## [1] "    indep_vars: biddable,sprice.root2"
## Loading required package: glmnet
## Loading required package: Matrix
## 
## Attaching package: 'Matrix'
## 
## The following object is masked from 'package:tidyr':
## 
##     expand
## 
## Loaded glmnet 2.0-2
## Fitting alpha = 0.1, lambda = 0.00576 on full training set

##             Length Class      Mode     
## a0           79    -none-     numeric  
## beta        158    dgCMatrix  S4       
## df           79    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       79    -none-     numeric  
## dev.ratio    79    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        2    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##  (Intercept)     biddable sprice.root2 
##    1.0277229    1.8660218   -0.1575942 
## [1] "max lambda < lambdaOpt:"
##  (Intercept)     biddable sprice.root2 
##    1.0358390    1.8708816   -0.1583838

##   sold.fctr sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.N
## 1         N                                          432
## 2         Y                                          104
##   sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.Y
## 1                                          116
## 2                                          368
##          Prediction
## Reference   N   Y
##         N 432 116
##         Y 104 368
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.843137e-01   5.669826e-01   7.577802e-01   8.091950e-01   5.372549e-01 
## AccuracyPValue  McnemarPValue 
##   1.365269e-60   4.583177e-01

##   sold.fctr sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.N
## 1         N                                          352
## 2         Y                                           94
##   sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.Y
## 1                                           95
## 2                                          289
##          Prediction
## Reference   N   Y
##         N 352  95
##         Y  94 289
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.722892e-01   5.419399e-01   7.422177e-01   8.004125e-01   5.385542e-01 
## AccuracyPValue  McnemarPValue 
##   2.113571e-44   1.000000e+00 
##                         id                 feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1.glmnet biddable,sprice.root2               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                       0.74                 0.023       0.7898522
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.8339416    0.7457627         0.86206                    0.4
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.7698745        0.7843137             0.7577802
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1              0.809195     0.5669826       0.7665376    0.8255034
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.7075718       0.8197236                    0.4       0.7535854
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.7722892             0.7422177             0.8004125
##   max.Kappa.OOB
## 1     0.5419399
# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
    for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
        ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats), 
            type = glb_model_type, trainControl.method = "repeatedcv",
            trainControl.number = rcv_n_folds, trainControl.repeats = rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            train.method = "glmnet", train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize)),
                            indep_vars = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                            fit_df = glb_fitobs_df, OOB_df = glb_OOBobs_df)
    }
## [1] "fitting model: Max.cor.Y.rcv.3X1.glmnet"
## [1] "    indep_vars: biddable,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.124 on full training set
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0           79    -none-     numeric  
## beta        158    dgCMatrix  S4       
## df           79    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       79    -none-     numeric  
## dev.ratio    79    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        2    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##  (Intercept)     biddable sprice.root2 
##   0.39977062   1.21150458  -0.08600408 
## [1] "max lambda < lambdaOpt:"
##  (Intercept)     biddable sprice.root2 
##    0.4231996    1.2510219   -0.0892442

##   sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.N
## 1         N                                          457
## 2         Y                                          118
##   sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.Y
## 1                                           91
## 2                                          354
##          Prediction
## Reference   N   Y
##         N 457  91
##         Y 118 354
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.950980e-01   5.862671e-01   7.690020e-01   8.194783e-01   5.372549e-01 
## AccuracyPValue  McnemarPValue 
##   3.331019e-66   7.210452e-02

##   sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.N
## 1         N                                          342
## 2         Y                                           89
##   sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.Y
## 1                                          105
## 2                                          294
##          Prediction
## Reference   N   Y
##         N 342 105
##         Y  89 294
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.662651e-01   5.311363e-01   7.359543e-01   7.946712e-01   5.385542e-01 
## AccuracyPValue  McnemarPValue 
##   4.058545e-42   2.815083e-01 
##                         id                 feats max.nTuningRuns
## 1 Max.cor.Y.rcv.3X1.glmnet biddable,sprice.root2              25
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      1.546                 0.018       0.7919708
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.8339416         0.75       0.8621335                    0.5
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.7720829        0.7950911              0.769002
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.8194783     0.5860164       0.7592654    0.8187919
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.6997389       0.8197353                    0.4       0.7519182
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.7662651             0.7359543             0.7946712
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1     0.5311363        0.003934893     0.009324777
## [1] "fitting model: Max.cor.Y.rcv.3X3.glmnet"
## [1] "    indep_vars: biddable,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.124 on full training set
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0           67    -none-     numeric  
## beta        134    dgCMatrix  S4       
## df           67    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       67    -none-     numeric  
## dev.ratio    67    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        2    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##  (Intercept)     biddable sprice.root2 
##   0.36344652   1.18295779  -0.08216503 
## [1] "max lambda < lambdaOpt:"
##  (Intercept)     biddable sprice.root2 
##   0.39118688   1.22752496  -0.08592317

##   sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.N
## 1         N                                          457
## 2         Y                                          118
##   sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.Y
## 1                                           91
## 2                                          354
##          Prediction
## Reference   N   Y
##         N 457  91
##         Y 118 354
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.950980e-01   5.862671e-01   7.690020e-01   8.194783e-01   5.372549e-01 
## AccuracyPValue  McnemarPValue 
##   3.331019e-66   7.210452e-02

##   sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.N
## 1         N                                          341
## 2         Y                                           88
##   sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.Y
## 1                                          106
## 2                                          295
##          Prediction
## Reference   N   Y
##         N 341 106
##         Y  88 295
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.662651e-01   5.313109e-01   7.359543e-01   7.946712e-01   5.385542e-01 
## AccuracyPValue  McnemarPValue 
##   4.058545e-42   2.222645e-01 
##                         id                 feats max.nTuningRuns
## 1 Max.cor.Y.rcv.3X3.glmnet biddable,sprice.root2              25
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      2.078                 0.016       0.7919708
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.8339416         0.75       0.8622147                    0.5
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.7720829        0.7954221              0.769002
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.8194783     0.5866808        0.760384    0.8210291
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.6997389        0.819747                    0.4        0.752551
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.7662651             0.7359543             0.7946712
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1     0.5313109          0.0200887      0.04087803
## [1] "fitting model: Max.cor.Y.rcv.3X5.glmnet"
## [1] "    indep_vars: biddable,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.124 on full training set
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0           61    -none-     numeric  
## beta        122    dgCMatrix  S4       
## df           61    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       61    -none-     numeric  
## dev.ratio    61    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        2    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##  (Intercept)     biddable sprice.root2 
##   0.28887900   1.10455877  -0.07355771 
## [1] "max lambda < lambdaOpt:"
##  (Intercept)     biddable sprice.root2 
##   0.32191930   1.15672712  -0.07800633

##   sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.N
## 1         N                                          457
## 2         Y                                          118
##   sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.Y
## 1                                           91
## 2                                          354
##          Prediction
## Reference   N   Y
##         N 457  91
##         Y 118 354
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.950980e-01   5.862671e-01   7.690020e-01   8.194783e-01   5.372549e-01 
## AccuracyPValue  McnemarPValue 
##   3.331019e-66   7.210452e-02

##   sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.N
## 1         N                                          340
## 2         Y                                           88
##   sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.Y
## 1                                          107
## 2                                          295
##          Prediction
## Reference   N   Y
##         N 340 107
##         Y  88 295
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.650602e-01   5.289828e-01   7.347026e-01   7.935220e-01   5.385542e-01 
## AccuracyPValue  McnemarPValue 
##   1.138374e-41   1.973957e-01 
##                         id                 feats max.nTuningRuns
## 1 Max.cor.Y.rcv.3X5.glmnet biddable,sprice.root2              25
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      2.568                 0.016       0.7919708
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.8339416         0.75       0.8622069                    0.5
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.7720829        0.7962655              0.769002
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.8194783       0.58834        0.760384    0.8210291
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.6997389       0.8196593                    0.4       0.7515924
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.7650602             0.7347026              0.793522
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1     0.5289828         0.01812066      0.03689711
## [1] "fitting model: Max.cor.Y.rcv.5X1.glmnet"
## [1] "    indep_vars: biddable,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.124 on full training set
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0           57    -none-     numeric  
## beta        114    dgCMatrix  S4       
## df           57    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       57    -none-     numeric  
## dev.ratio    57    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        2    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##  (Intercept)     biddable sprice.root2 
##   0.19570698   1.01735782  -0.06318327 
## [1] "max lambda < lambdaOpt:"
##  (Intercept)     biddable sprice.root2 
##   0.23591040   1.07863496  -0.06852179

##   sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.N
## 1         N                                          461
## 2         Y                                          118
##   sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.Y
## 1                                           87
## 2                                          354
##          Prediction
## Reference   N   Y
##         N 461  87
##         Y 118 354
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.990196e-01   5.939459e-01   7.730893e-01   8.232111e-01   5.372549e-01 
## AccuracyPValue  McnemarPValue 
##   2.536091e-68   3.614514e-02

##   sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.N
## 1         N                                          339
## 2         Y                                           88
##   sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.Y
## 1                                          108
## 2                                          295
##          Prediction
## Reference   N   Y
##         N 339 108
##         Y  88 295
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.638554e-01   5.266555e-01   7.334512e-01   7.923725e-01   5.385542e-01 
## AccuracyPValue  McnemarPValue 
##   3.171799e-41   1.747358e-01 
##                         id                 feats max.nTuningRuns
## 1 Max.cor.Y.rcv.5X1.glmnet biddable,sprice.root2              25
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      1.729                 0.015       0.7956204
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.8412409         0.75       0.8621064                    0.5
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.7754655        0.7990899             0.7730893
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.8232111     0.5944245       0.7615026    0.8232662
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.6997389         0.81956                    0.4       0.7506361
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.7638554             0.7334512             0.7923725
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1     0.5266555         0.03608644      0.07068726
## [1] "fitting model: Max.cor.Y.rcv.5X3.glmnet"
## [1] "    indep_vars: biddable,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.124 on full training set
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0           57    -none-     numeric  
## beta        114    dgCMatrix  S4       
## df           57    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       57    -none-     numeric  
## dev.ratio    57    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        2    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##  (Intercept)     biddable sprice.root2 
##   0.19570698   1.01735782  -0.06318327 
## [1] "max lambda < lambdaOpt:"
##  (Intercept)     biddable sprice.root2 
##   0.23591040   1.07863496  -0.06852179

##   sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.N
## 1         N                                          461
## 2         Y                                          118
##   sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.Y
## 1                                           87
## 2                                          354
##          Prediction
## Reference   N   Y
##         N 461  87
##         Y 118 354
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.990196e-01   5.939459e-01   7.730893e-01   8.232111e-01   5.372549e-01 
## AccuracyPValue  McnemarPValue 
##   2.536091e-68   3.614514e-02

##   sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.N
## 1         N                                          339
## 2         Y                                           88
##   sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.Y
## 1                                          108
## 2                                          295
##          Prediction
## Reference   N   Y
##         N 339 108
##         Y  88 295
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.638554e-01   5.266555e-01   7.334512e-01   7.923725e-01   5.385542e-01 
## AccuracyPValue  McnemarPValue 
##   3.171799e-41   1.747358e-01 
##                         id                 feats max.nTuningRuns
## 1 Max.cor.Y.rcv.5X3.glmnet biddable,sprice.root2              25
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      2.597                 0.016       0.7956204
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.8412409         0.75       0.8621064                    0.5
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.7754655        0.7964364             0.7730893
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.8232111     0.5888026       0.7615026    0.8232662
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.6997389         0.81956                    0.4       0.7506361
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.7638554             0.7334512             0.7923725
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1     0.5266555         0.03349187      0.06729278
## [1] "fitting model: Max.cor.Y.rcv.5X5.glmnet"
## [1] "    indep_vars: biddable,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.124 on full training set
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0           54    -none-     numeric  
## beta        108    dgCMatrix  S4       
## df           54    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       54    -none-     numeric  
## dev.ratio    54    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        2    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##  (Intercept)     biddable sprice.root2 
##   0.12532879   0.99816406  -0.05704275 
## [1] "max lambda < lambdaOpt:"
##  (Intercept)     biddable sprice.root2 
##   0.17511926   1.06475879  -0.06331821

##   sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.N
## 1         N                                          463
## 2         Y                                          119
##   sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.Y
## 1                                           85
## 2                                          353
##          Prediction
## Reference   N   Y
##         N 463  85
##         Y 119 353
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   8.000000e-01   5.957477e-01   7.741117e-01   8.241437e-01   5.372549e-01 
## AccuracyPValue  McnemarPValue 
##   7.378773e-69   2.086258e-02

##   sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.N
## 1         N                                          338
## 2         Y                                           87
##   sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.Y
## 1                                          109
## 2                                          296
##          Prediction
## Reference   N   Y
##         N 338 109
##         Y  87 296
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.638554e-01   5.268317e-01   7.334512e-01   7.923725e-01   5.385542e-01 
## AccuracyPValue  McnemarPValue 
##   3.171799e-41   1.336144e-01 
##                         id                 feats max.nTuningRuns
## 1 Max.cor.Y.rcv.5X5.glmnet biddable,sprice.root2              25
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      3.111                 0.015       0.7963859
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.8448905    0.7478814       0.8621064                    0.5
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.7758242        0.7970687             0.7741117
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.8241437     0.5901123       0.7641135    0.8232662
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.7049608        0.819379                    0.4        0.751269
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.7638554             0.7334512             0.7923725
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1     0.5268317         0.03129087      0.06285747
# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
print(myplot_parcoord(obs_df = subset(glb_models_df, 
                                      grepl("Max.cor.Y.rcv.", id, fixed = TRUE), 
                                        select = -feats)[, tmp_models_cols],
                      id_var = "id"))

ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
    id.prefix="Max.cor.Y.rcv.1X1.cp.0", type=glb_model_type, trainControl.method="none",
    train.method="rpart",
    tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
                    indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var, 
                    fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
## [1] "fitting model: Max.cor.Y.rcv.1X1.cp.0.rpart"
## [1] "    indep_vars: biddable,sprice.root2"
## Loading required package: rpart
## Fitting cp = 0 on full training set
## Loading required package: rpart.plot

## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 1020 
## 
##            CP nsplit rel error
## 1 0.546610169      0 1.0000000
## 2 0.025423729      1 0.4533898
## 3 0.010593220      2 0.4279661
## 4 0.006355932      5 0.3961864
## 5 0.003531073      6 0.3898305
## 6 0.002118644      9 0.3792373
## 7 0.001059322     16 0.3644068
## 8 0.000000000     20 0.3601695
## 
## Variable importance
##     biddable sprice.root2 
##           52           48 
## 
## Node number 1: 1020 observations,    complexity param=0.5466102
##   predicted class=N  expected loss=0.4627451  P(node) =1
##     class counts:   548   472
##    probabilities: 0.537 0.463 
##   left son=2 (554 obs) right son=3 (466 obs)
##   Primary splits:
##       biddable     < 0.5      to the left,  improve=169.2715, (0 missing)
##       sprice.root2 < 10.23963 to the right, improve=133.6444, (0 missing)
##   Surrogate splits:
##       sprice.root2 < 12.26518 to the right, agree=0.751, adj=0.455, (0 split)
## 
## Node number 2: 554 observations,    complexity param=0.003531073
##   predicted class=N  expected loss=0.198556  P(node) =0.5431373
##     class counts:   444   110
##    probabilities: 0.801 0.199 
##   left son=4 (124 obs) right son=5 (430 obs)
##   Primary splits:
##       sprice.root2 < 19.90914 to the right, improve=3.855349, (0 missing)
## 
## Node number 3: 466 observations,    complexity param=0.02542373
##   predicted class=Y  expected loss=0.223176  P(node) =0.4568627
##     class counts:   104   362
##    probabilities: 0.223 0.777 
##   left son=6 (164 obs) right son=7 (302 obs)
##   Primary splits:
##       sprice.root2 < 11.83195 to the right, improve=49.71379, (0 missing)
## 
## Node number 4: 124 observations
##   predicted class=N  expected loss=0.08870968  P(node) =0.1215686
##     class counts:   113    11
##    probabilities: 0.911 0.089 
## 
## Node number 5: 430 observations,    complexity param=0.003531073
##   predicted class=N  expected loss=0.2302326  P(node) =0.4215686
##     class counts:   331    99
##    probabilities: 0.770 0.230 
##   left son=10 (263 obs) right son=11 (167 obs)
##   Primary splits:
##       sprice.root2 < 13.61047 to the right, improve=1.786236, (0 missing)
## 
## Node number 6: 164 observations,    complexity param=0.01059322
##   predicted class=N  expected loss=0.4634146  P(node) =0.1607843
##     class counts:    88    76
##    probabilities: 0.537 0.463 
##   left son=12 (19 obs) right son=13 (145 obs)
##   Primary splits:
##       sprice.root2 < 20.33187 to the right, improve=2.748634, (0 missing)
## 
## Node number 7: 302 observations
##   predicted class=Y  expected loss=0.05298013  P(node) =0.2960784
##     class counts:    16   286
##    probabilities: 0.053 0.947 
## 
## Node number 10: 263 observations
##   predicted class=N  expected loss=0.1939163  P(node) =0.2578431
##     class counts:   212    51
##    probabilities: 0.806 0.194 
## 
## Node number 11: 167 observations,    complexity param=0.003531073
##   predicted class=N  expected loss=0.2874251  P(node) =0.1637255
##     class counts:   119    48
##    probabilities: 0.713 0.287 
##   left son=22 (158 obs) right son=23 (9 obs)
##   Primary splits:
##       sprice.root2 < 13.41548 to the left,  improve=4.574556, (0 missing)
## 
## Node number 12: 19 observations
##   predicted class=N  expected loss=0.2105263  P(node) =0.01862745
##     class counts:    15     4
##    probabilities: 0.789 0.211 
## 
## Node number 13: 145 observations,    complexity param=0.01059322
##   predicted class=N  expected loss=0.4965517  P(node) =0.1421569
##     class counts:    73    72
##    probabilities: 0.503 0.497 
##   left son=26 (87 obs) right son=27 (58 obs)
##   Primary splits:
##       sprice.root2 < 13.89077 to the right, improve=1.554023, (0 missing)
## 
## Node number 22: 158 observations,    complexity param=0.002118644
##   predicted class=N  expected loss=0.2594937  P(node) =0.154902
##     class counts:   117    41
##    probabilities: 0.741 0.259 
##   left son=44 (30 obs) right son=45 (128 obs)
##   Primary splits:
##       sprice.root2 < 12.54832 to the right, improve=1.884019, (0 missing)
## 
## Node number 23: 9 observations
##   predicted class=Y  expected loss=0.2222222  P(node) =0.008823529
##     class counts:     2     7
##    probabilities: 0.222 0.778 
## 
## Node number 26: 87 observations,    complexity param=0.01059322
##   predicted class=N  expected loss=0.4367816  P(node) =0.08529412
##     class counts:    49    38
##    probabilities: 0.563 0.437 
##   left son=52 (48 obs) right son=53 (39 obs)
##   Primary splits:
##       sprice.root2 < 16.50717 to the left,  improve=2.291777, (0 missing)
## 
## Node number 27: 58 observations,    complexity param=0.006355932
##   predicted class=Y  expected loss=0.4137931  P(node) =0.05686275
##     class counts:    24    34
##    probabilities: 0.414 0.586 
##   left son=54 (9 obs) right son=55 (49 obs)
##   Primary splits:
##       sprice.root2 < 12.24684 to the left,  improve=1.362421, (0 missing)
## 
## Node number 44: 30 observations
##   predicted class=N  expected loss=0.1  P(node) =0.02941176
##     class counts:    27     3
##    probabilities: 0.900 0.100 
## 
## Node number 45: 128 observations,    complexity param=0.002118644
##   predicted class=N  expected loss=0.296875  P(node) =0.1254902
##     class counts:    90    38
##    probabilities: 0.703 0.297 
##   left son=90 (95 obs) right son=91 (33 obs)
##   Primary splits:
##       sprice.root2 < 11.40153 to the left,  improve=2.210706, (0 missing)
## 
## Node number 52: 48 observations,    complexity param=0.001059322
##   predicted class=N  expected loss=0.3333333  P(node) =0.04705882
##     class counts:    32    16
##    probabilities: 0.667 0.333 
##   left son=104 (7 obs) right son=105 (41 obs)
##   Primary splits:
##       sprice.root2 < 15.89005 to the right, improve=0.5946574, (0 missing)
## 
## Node number 53: 39 observations,    complexity param=0.002118644
##   predicted class=Y  expected loss=0.4358974  P(node) =0.03823529
##     class counts:    17    22
##    probabilities: 0.436 0.564 
##   left son=106 (15 obs) right son=107 (24 obs)
##   Primary splits:
##       sprice.root2 < 18.70815 to the right, improve=0.4628205, (0 missing)
## 
## Node number 54: 9 observations
##   predicted class=N  expected loss=0.3333333  P(node) =0.008823529
##     class counts:     6     3
##    probabilities: 0.667 0.333 
## 
## Node number 55: 49 observations
##   predicted class=Y  expected loss=0.3673469  P(node) =0.04803922
##     class counts:    18    31
##    probabilities: 0.367 0.633 
## 
## Node number 90: 95 observations,    complexity param=0.002118644
##   predicted class=N  expected loss=0.2421053  P(node) =0.09313725
##     class counts:    72    23
##    probabilities: 0.758 0.242 
##   left son=180 (23 obs) right son=181 (72 obs)
##   Primary splits:
##       sprice.root2 < 10.23963 to the right, improve=1.460984, (0 missing)
## 
## Node number 91: 33 observations,    complexity param=0.002118644
##   predicted class=N  expected loss=0.4545455  P(node) =0.03235294
##     class counts:    18    15
##    probabilities: 0.545 0.455 
##   left son=182 (26 obs) right son=183 (7 obs)
##   Primary splits:
##       sprice.root2 < 11.81057 to the right, improve=1.198801, (0 missing)
## 
## Node number 104: 7 observations
##   predicted class=N  expected loss=0.1428571  P(node) =0.006862745
##     class counts:     6     1
##    probabilities: 0.857 0.143 
## 
## Node number 105: 41 observations,    complexity param=0.001059322
##   predicted class=N  expected loss=0.3658537  P(node) =0.04019608
##     class counts:    26    15
##    probabilities: 0.634 0.366 
##   left son=210 (32 obs) right son=211 (9 obs)
##   Primary splits:
##       sprice.root2 < 15.8106  to the left,  improve=0.8299458, (0 missing)
## 
## Node number 106: 15 observations
##   predicted class=N  expected loss=0.4666667  P(node) =0.01470588
##     class counts:     8     7
##    probabilities: 0.533 0.467 
## 
## Node number 107: 24 observations
##   predicted class=Y  expected loss=0.375  P(node) =0.02352941
##     class counts:     9    15
##    probabilities: 0.375 0.625 
## 
## Node number 180: 23 observations
##   predicted class=N  expected loss=0.08695652  P(node) =0.02254902
##     class counts:    21     2
##    probabilities: 0.913 0.087 
## 
## Node number 181: 72 observations,    complexity param=0.002118644
##   predicted class=N  expected loss=0.2916667  P(node) =0.07058824
##     class counts:    51    21
##    probabilities: 0.708 0.292 
##   left son=362 (10 obs) right son=363 (62 obs)
##   Primary splits:
##       sprice.root2 < 5.417281 to the left,  improve=0.8532258, (0 missing)
## 
## Node number 182: 26 observations
##   predicted class=N  expected loss=0.3846154  P(node) =0.0254902
##     class counts:    16    10
##    probabilities: 0.615 0.385 
## 
## Node number 183: 7 observations
##   predicted class=Y  expected loss=0.2857143  P(node) =0.006862745
##     class counts:     2     5
##    probabilities: 0.286 0.714 
## 
## Node number 210: 32 observations
##   predicted class=N  expected loss=0.3125  P(node) =0.03137255
##     class counts:    22    10
##    probabilities: 0.688 0.312 
## 
## Node number 211: 9 observations
##   predicted class=Y  expected loss=0.4444444  P(node) =0.008823529
##     class counts:     4     5
##    probabilities: 0.444 0.556 
## 
## Node number 362: 10 observations
##   predicted class=N  expected loss=0.1  P(node) =0.009803922
##     class counts:     9     1
##    probabilities: 0.900 0.100 
## 
## Node number 363: 62 observations,    complexity param=0.002118644
##   predicted class=N  expected loss=0.3225806  P(node) =0.06078431
##     class counts:    42    20
##    probabilities: 0.677 0.323 
##   left son=726 (55 obs) right son=727 (7 obs)
##   Primary splits:
##       sprice.root2 < 7.602942 to the right, improve=2.42145, (0 missing)
## 
## Node number 726: 55 observations,    complexity param=0.001059322
##   predicted class=N  expected loss=0.2727273  P(node) =0.05392157
##     class counts:    40    15
##    probabilities: 0.727 0.273 
##   left son=1452 (29 obs) right son=1453 (26 obs)
##   Primary splits:
##       sprice.root2 < 9.486569 to the left,  improve=1.234627, (0 missing)
## 
## Node number 727: 7 observations
##   predicted class=Y  expected loss=0.2857143  P(node) =0.006862745
##     class counts:     2     5
##    probabilities: 0.286 0.714 
## 
## Node number 1452: 29 observations
##   predicted class=N  expected loss=0.1724138  P(node) =0.02843137
##     class counts:    24     5
##    probabilities: 0.828 0.172 
## 
## Node number 1453: 26 observations,    complexity param=0.001059322
##   predicted class=N  expected loss=0.3846154  P(node) =0.0254902
##     class counts:    16    10
##    probabilities: 0.615 0.385 
##   left son=2906 (19 obs) right son=2907 (7 obs)
##   Primary splits:
##       sprice.root2 < 9.565528 to the right, improve=0.6685946, (0 missing)
## 
## Node number 2906: 19 observations
##   predicted class=N  expected loss=0.3157895  P(node) =0.01862745
##     class counts:    13     6
##    probabilities: 0.684 0.316 
## 
## Node number 2907: 7 observations
##   predicted class=Y  expected loss=0.4285714  P(node) =0.006862745
##     class counts:     3     4
##    probabilities: 0.429 0.571 
## 
## n= 1020 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
##    1) root 1020 472 N (0.53725490 0.46274510)  
##      2) biddable< 0.5 554 110 N (0.80144404 0.19855596)  
##        4) sprice.root2>=19.90914 124  11 N (0.91129032 0.08870968) *
##        5) sprice.root2< 19.90914 430  99 N (0.76976744 0.23023256)  
##         10) sprice.root2>=13.61047 263  51 N (0.80608365 0.19391635) *
##         11) sprice.root2< 13.61047 167  48 N (0.71257485 0.28742515)  
##           22) sprice.root2< 13.41548 158  41 N (0.74050633 0.25949367)  
##             44) sprice.root2>=12.54832 30   3 N (0.90000000 0.10000000) *
##             45) sprice.root2< 12.54832 128  38 N (0.70312500 0.29687500)  
##               90) sprice.root2< 11.40153 95  23 N (0.75789474 0.24210526)  
##                180) sprice.root2>=10.23963 23   2 N (0.91304348 0.08695652) *
##                181) sprice.root2< 10.23963 72  21 N (0.70833333 0.29166667)  
##                  362) sprice.root2< 5.417281 10   1 N (0.90000000 0.10000000) *
##                  363) sprice.root2>=5.417281 62  20 N (0.67741935 0.32258065)  
##                    726) sprice.root2>=7.602942 55  15 N (0.72727273 0.27272727)  
##                     1452) sprice.root2< 9.486569 29   5 N (0.82758621 0.17241379) *
##                     1453) sprice.root2>=9.486569 26  10 N (0.61538462 0.38461538)  
##                       2906) sprice.root2>=9.565528 19   6 N (0.68421053 0.31578947) *
##                       2907) sprice.root2< 9.565528 7   3 Y (0.42857143 0.57142857) *
##                    727) sprice.root2< 7.602942 7   2 Y (0.28571429 0.71428571) *
##               91) sprice.root2>=11.40153 33  15 N (0.54545455 0.45454545)  
##                182) sprice.root2>=11.81057 26  10 N (0.61538462 0.38461538) *
##                183) sprice.root2< 11.81057 7   2 Y (0.28571429 0.71428571) *
##           23) sprice.root2>=13.41548 9   2 Y (0.22222222 0.77777778) *
##      3) biddable>=0.5 466 104 Y (0.22317597 0.77682403)  
##        6) sprice.root2>=11.83195 164  76 N (0.53658537 0.46341463)  
##         12) sprice.root2>=20.33187 19   4 N (0.78947368 0.21052632) *
##         13) sprice.root2< 20.33187 145  72 N (0.50344828 0.49655172)  
##           26) sprice.root2>=13.89077 87  38 N (0.56321839 0.43678161)  
##             52) sprice.root2< 16.50717 48  16 N (0.66666667 0.33333333)  
##              104) sprice.root2>=15.89005 7   1 N (0.85714286 0.14285714) *
##              105) sprice.root2< 15.89005 41  15 N (0.63414634 0.36585366)  
##                210) sprice.root2< 15.8106 32  10 N (0.68750000 0.31250000) *
##                211) sprice.root2>=15.8106 9   4 Y (0.44444444 0.55555556) *
##             53) sprice.root2>=16.50717 39  17 Y (0.43589744 0.56410256)  
##              106) sprice.root2>=18.70815 15   7 N (0.53333333 0.46666667) *
##              107) sprice.root2< 18.70815 24   9 Y (0.37500000 0.62500000) *
##           27) sprice.root2< 13.89077 58  24 Y (0.41379310 0.58620690)  
##             54) sprice.root2< 12.24684 9   3 N (0.66666667 0.33333333) *
##             55) sprice.root2>=12.24684 49  18 Y (0.36734694 0.63265306) *
##        7) sprice.root2< 11.83195 302  16 Y (0.05298013 0.94701987) *

##   sold.fctr sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.N
## 1         N                                              484
## 2         Y                                              107
##   sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.Y
## 1                                               64
## 2                                              365
##          Prediction
## Reference   N   Y
##         N 484  64
##         Y 107 365
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   8.323529e-01   6.606905e-01   8.079872e-01   8.547824e-01   5.372549e-01 
## AccuracyPValue  McnemarPValue 
##   4.198532e-88   1.318969e-03

##   sold.fctr sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.N
## 1         N                                              327
## 2         Y                                               93
##   sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.Y
## 1                                              120
## 2                                              290
##          Prediction
## Reference   N   Y
##         N 327 120
##         Y  93 290
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.433735e-01   4.862697e-01   7.122254e-01   7.727821e-01   5.385542e-01 
## AccuracyPValue  McnemarPValue 
##   4.300069e-34   7.483233e-02 
##                             id                 feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1.cp.0.rpart biddable,sprice.root2               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                       0.75                 0.012       0.8281424
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.8978102    0.7584746       0.8816304                    0.4
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.8102109        0.8323529             0.8079872
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.8547824     0.6606905        0.750495    0.8456376
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.6553525       0.7934621                    0.3       0.7313997
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.7433735             0.7122254             0.7727821
##   max.Kappa.OOB
## 1     0.4862697
#stop(here"); glb_to_sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
    id.prefix="Max.cor.Y", 
    type=glb_model_type, trainControl.method="repeatedcv",
    trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
    train.method="rpart")),
    indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var, 
    fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
## [1] "fitting model: Max.cor.Y.rpart"
## [1] "    indep_vars: biddable,sprice.root2"
## + Fold1.Rep1: cp=0.003531 
## - Fold1.Rep1: cp=0.003531 
## + Fold2.Rep1: cp=0.003531 
## - Fold2.Rep1: cp=0.003531 
## + Fold3.Rep1: cp=0.003531 
## - Fold3.Rep1: cp=0.003531 
## + Fold1.Rep2: cp=0.003531 
## - Fold1.Rep2: cp=0.003531 
## + Fold2.Rep2: cp=0.003531 
## - Fold2.Rep2: cp=0.003531 
## + Fold3.Rep2: cp=0.003531 
## - Fold3.Rep2: cp=0.003531 
## + Fold1.Rep3: cp=0.003531 
## - Fold1.Rep3: cp=0.003531 
## + Fold2.Rep3: cp=0.003531 
## - Fold2.Rep3: cp=0.003531 
## + Fold3.Rep3: cp=0.003531 
## - Fold3.Rep3: cp=0.003531 
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00636 on full training set

## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 1020 
## 
##            CP nsplit rel error
## 1 0.546610169      0 1.0000000
## 2 0.025423729      1 0.4533898
## 3 0.010593220      2 0.4279661
## 4 0.006355932      5 0.3961864
## 
## Variable importance
##     biddable sprice.root2 
##           56           44 
## 
## Node number 1: 1020 observations,    complexity param=0.5466102
##   predicted class=N  expected loss=0.4627451  P(node) =1
##     class counts:   548   472
##    probabilities: 0.537 0.463 
##   left son=2 (554 obs) right son=3 (466 obs)
##   Primary splits:
##       biddable     < 0.5      to the left,  improve=169.2715, (0 missing)
##       sprice.root2 < 10.23963 to the right, improve=133.6444, (0 missing)
##   Surrogate splits:
##       sprice.root2 < 12.26518 to the right, agree=0.751, adj=0.455, (0 split)
## 
## Node number 2: 554 observations
##   predicted class=N  expected loss=0.198556  P(node) =0.5431373
##     class counts:   444   110
##    probabilities: 0.801 0.199 
## 
## Node number 3: 466 observations,    complexity param=0.02542373
##   predicted class=Y  expected loss=0.223176  P(node) =0.4568627
##     class counts:   104   362
##    probabilities: 0.223 0.777 
##   left son=6 (164 obs) right son=7 (302 obs)
##   Primary splits:
##       sprice.root2 < 11.83195 to the right, improve=49.71379, (0 missing)
## 
## Node number 6: 164 observations,    complexity param=0.01059322
##   predicted class=N  expected loss=0.4634146  P(node) =0.1607843
##     class counts:    88    76
##    probabilities: 0.537 0.463 
##   left son=12 (19 obs) right son=13 (145 obs)
##   Primary splits:
##       sprice.root2 < 20.33187 to the right, improve=2.748634, (0 missing)
## 
## Node number 7: 302 observations
##   predicted class=Y  expected loss=0.05298013  P(node) =0.2960784
##     class counts:    16   286
##    probabilities: 0.053 0.947 
## 
## Node number 12: 19 observations
##   predicted class=N  expected loss=0.2105263  P(node) =0.01862745
##     class counts:    15     4
##    probabilities: 0.789 0.211 
## 
## Node number 13: 145 observations,    complexity param=0.01059322
##   predicted class=N  expected loss=0.4965517  P(node) =0.1421569
##     class counts:    73    72
##    probabilities: 0.503 0.497 
##   left son=26 (87 obs) right son=27 (58 obs)
##   Primary splits:
##       sprice.root2 < 13.89077 to the right, improve=1.554023, (0 missing)
## 
## Node number 26: 87 observations,    complexity param=0.01059322
##   predicted class=N  expected loss=0.4367816  P(node) =0.08529412
##     class counts:    49    38
##    probabilities: 0.563 0.437 
##   left son=52 (48 obs) right son=53 (39 obs)
##   Primary splits:
##       sprice.root2 < 16.50717 to the left,  improve=2.291777, (0 missing)
## 
## Node number 27: 58 observations
##   predicted class=Y  expected loss=0.4137931  P(node) =0.05686275
##     class counts:    24    34
##    probabilities: 0.414 0.586 
## 
## Node number 52: 48 observations
##   predicted class=N  expected loss=0.3333333  P(node) =0.04705882
##     class counts:    32    16
##    probabilities: 0.667 0.333 
## 
## Node number 53: 39 observations
##   predicted class=Y  expected loss=0.4358974  P(node) =0.03823529
##     class counts:    17    22
##    probabilities: 0.436 0.564 
## 
## n= 1020 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
##  1) root 1020 472 N (0.53725490 0.46274510)  
##    2) biddable< 0.5 554 110 N (0.80144404 0.19855596) *
##    3) biddable>=0.5 466 104 Y (0.22317597 0.77682403)  
##      6) sprice.root2>=11.83195 164  76 N (0.53658537 0.46341463)  
##       12) sprice.root2>=20.33187 19   4 N (0.78947368 0.21052632) *
##       13) sprice.root2< 20.33187 145  72 N (0.50344828 0.49655172)  
##         26) sprice.root2>=13.89077 87  38 N (0.56321839 0.43678161)  
##           52) sprice.root2< 16.50717 48  16 N (0.66666667 0.33333333) *
##           53) sprice.root2>=16.50717 39  17 Y (0.43589744 0.56410256) *
##         27) sprice.root2< 13.89077 58  24 Y (0.41379310 0.58620690) *
##      7) sprice.root2< 11.83195 302  16 Y (0.05298013 0.94701987) *

##   sold.fctr sold.fctr.predict.Max.cor.Y.rpart.N
## 1         N                                 491
## 2         Y                                 130
##   sold.fctr.predict.Max.cor.Y.rpart.Y
## 1                                  57
## 2                                 342
##          Prediction
## Reference   N   Y
##         N 491  57
##         Y 130 342
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   8.166667e-01   6.272891e-01   7.915283e-01   8.399619e-01   5.372549e-01 
## AccuracyPValue  McnemarPValue 
##   2.183067e-78   1.400662e-07

##   sold.fctr sold.fctr.predict.Max.cor.Y.rpart.N
## 1         N                                 368
## 2         Y                                 112
##   sold.fctr.predict.Max.cor.Y.rpart.Y
## 1                                  79
## 2                                 271
##          Prediction
## Reference   N   Y
##         N 368  79
##         Y 112 271
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.698795e-01   5.341327e-01   7.397113e-01   7.981170e-01   5.385542e-01 
## AccuracyPValue  McnemarPValue 
##   1.766677e-43   2.058893e-02 
##                id                 feats max.nTuningRuns
## 1 Max.cor.Y.rpart biddable,sprice.root2               5
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      1.632                 0.013       0.8102808
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.8959854    0.7245763       0.8387008                    0.5
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.7853042        0.7964073             0.7915283
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.8399619     0.5852116       0.7445254    0.8545861
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.6344648       0.7855854                    0.3        0.739427
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.7698795             0.7397113              0.798117
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1     0.5341327         0.02006899      0.04005915
if (!is.null(glb_date_vars) && 
    (sum(grepl(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
               names(glb_allobs_df))) > 0)) {
# ret_lst <- myfit_mdl(mdl_id="Max.cor.Y.TmSrs.poly1", 
#                         model_method=ifelse(glb_is_regression, "lm", 
#                                         ifelse(glb_is_binomial, "glm", "rpart")),
#                      model_type=glb_model_type,
#                         indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
#                         rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
#                         fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
#                         n_cv_folds=glb_rcv_n_folds, tune_models_df=NULL)
# 
ret_lst <- myfit_mdl(mdl_id="Max.cor.Y.TmSrs.poly", 
                        model_method=ifelse(glb_is_regression, "lm", 
                                        ifelse(glb_is_binomial, "glm", "rpart")),
                     model_type=glb_model_type,
                        indep_vars_vctr=c(max_cor_y_x_vars, 
            grep(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
                        names(glb_allobs_df), value=TRUE)),
                        rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
                        fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
                        n_cv_folds=glb_rcv_n_folds, tune_models_df=NULL)
}

# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA), 
                                subset(glb_feats_df, nzv)$id)) > 0) {
    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
        id.prefix="Interact.High.cor.Y", 
        type=glb_model_type, trainControl.method="repeatedcv",
        trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
        train.method="glmnet")),
        indep_vars=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
        rsp_var=glb_rsp_var, 
        fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
}    
## [1] "fitting model: Interact.High.cor.Y.glmnet"
## [1] "    indep_vars: biddable,sprice.root2,biddable:sprice.log10,biddable:D.terms.post.stop.n.log,biddable:startprice.dcm2.is9,biddable:D.weight.post.stem.sum,biddable:D.chrs.n.log,biddable:cellular.fctr,biddable:D.terms.post.stem.n.log,biddable:sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.00124 on full training set

##             Length Class      Mode     
## a0           100   -none-     numeric  
## beta        1100   dgCMatrix  S4       
## df           100   -none-     numeric  
## dim            2   -none-     numeric  
## lambda       100   -none-     numeric  
## dev.ratio    100   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## classnames     2   -none-     character
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        11   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      2   -none-     character
## [1] "min lambda > lambdaOpt:"
##                      (Intercept)                         biddable 
##                       0.01092205                       4.74489316 
##                     sprice.root2            biddable:D.chrs.n.log 
##                      -0.08925084                       0.07353070 
## biddable:D.terms.post.stop.n.log  biddable:D.weight.post.stem.sum 
##                      -0.03122283                      -0.14511418 
##          biddable:cellular.fctr1    biddable:cellular.fctrUnknown 
##                      -0.13728156                      -1.05190220 
##            biddable:sprice.log10            biddable:sprice.root2 
##                      -0.10196500                      -0.15254153 
##     biddable:startprice.dcm2.is9 
##                      -0.04646263 
## [1] "max lambda < lambdaOpt:"
##                      (Intercept)                         biddable 
##                     -0.002342312                      4.814280953 
##                     sprice.root2            biddable:D.chrs.n.log 
##                     -0.088443437                      0.099967006 
## biddable:D.terms.post.stop.n.log  biddable:D.weight.post.stem.sum 
##                     -0.057642263                     -0.156438317 
##          biddable:cellular.fctr1    biddable:cellular.fctrUnknown 
##                     -0.141662526                     -1.060718650 
##            biddable:sprice.log10            biddable:sprice.root2 
##                     -0.113943290                     -0.152574470 
##     biddable:startprice.dcm2.is9 
##                     -0.056816165

##   sold.fctr sold.fctr.predict.Interact.High.cor.Y.glmnet.N
## 1         N                                            467
## 2         Y                                            124
##   sold.fctr.predict.Interact.High.cor.Y.glmnet.Y
## 1                                             81
## 2                                            348
##          Prediction
## Reference   N   Y
##         N 467  81
##         Y 124 348
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.990196e-01   5.932255e-01   7.730893e-01   8.232111e-01   5.372549e-01 
## AccuracyPValue  McnemarPValue 
##   2.536091e-68   3.352638e-03

##   sold.fctr sold.fctr.predict.Interact.High.cor.Y.glmnet.N
## 1         N                                            349
## 2         Y                                             93
##   sold.fctr.predict.Interact.High.cor.Y.glmnet.Y
## 1                                             98
## 2                                            290
##          Prediction
## Reference   N   Y
##         N 349  98
##         Y  93 290
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.698795e-01   5.374385e-01   7.397113e-01   7.981170e-01   5.385542e-01 
## AccuracyPValue  McnemarPValue 
##   1.766677e-43   7.722525e-01 
##                           id
## 1 Interact.High.cor.Y.glmnet
##                                                                                                                                                                                                                                           feats
## 1 biddable,sprice.root2,biddable:sprice.log10,biddable:D.terms.post.stop.n.log,biddable:startprice.dcm2.is9,biddable:D.weight.post.stem.sum,biddable:D.chrs.n.log,biddable:cellular.fctr,biddable:D.terms.post.stem.n.log,biddable:sprice.root2
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                      2.747                   0.1
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.7970973    0.8886861    0.7055085        0.863937
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.4        0.772475        0.8026098
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.7730893             0.8232111     0.5988972
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.7551533    0.8680089    0.6422977          0.8189
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.3       0.7522698        0.7698795
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.7397113              0.798117     0.5374385
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01796977      0.03705653
# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
#     indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) & 
#                                             is.ConditionalX.y & 
#                                             (exclude.as.feat != 1))[, "id"] else
indep_vars <- subset(glb_feats_df, is.na(cor.high.X) & !nzv & 
                              (exclude.as.feat != 1))[, "id"]  
indep_vars <- myadjust_interaction_feats(indep_vars)
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
        id.prefix="Low.cor.X", 
        type=glb_model_type, trainControl.method="repeatedcv",
        trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
        train.method="glmnet")),
        indep_vars=indep_vars, rsp_var=glb_rsp_var, 
        fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
## [1] "fitting model: Low.cor.X.glmnet"
## [1] "    indep_vars: biddable,spdiff.cut.fctr,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,storage.fctr,D.chrs.pnct11.n.log,startprice.dgt2.is9,color.fctr,prdl.my.fctr,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.root2,prdl.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.00124 on full training set

##             Length Class      Mode     
## a0            86   -none-     numeric  
## beta        5332   dgCMatrix  S4       
## df            86   -none-     numeric  
## dim            2   -none-     numeric  
## lambda        86   -none-     numeric  
## dev.ratio     86   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## classnames     2   -none-     character
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        62   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      2   -none-     character
## [1] "min lambda > lambdaOpt:"
##                            (Intercept) 
##                             0.28483910 
##                                 .rnorm 
##                            -0.02167279 
##                    D.chrs.pnct11.n.log 
##                             0.40659853 
##              D.ratio.weight.sum.wrds.n 
##                            -0.11673547 
##                D.terms.post.stop.n.log 
##                            -0.04986918 
##           D.weight.sum.stem.stop.Ratio 
##                            -1.64844144 
##                      D.wrds.stop.n.log 
##                            -0.06590812 
##                               biddable 
##                             2.44322986 
##                   cellular.fctrUnknown 
##                            -1.42750817 
##                         color.fctrGold 
##                             0.05191392 
##                   color.fctrSpace Gray 
##                            -0.31389140 
##                      color.fctrUnknown 
##                             0.32139639 
##                        color.fctrWhite 
##                            -0.14084948 
## condition.fctrFor parts or not working 
##                            -0.63820054 
## condition.fctrManufacturer refurbished 
##                            -1.03703740 
##  condition.fctrNew other (see details) 
##                             0.53706086 
##       condition.fctrSeller refurbished 
##                            -0.92779447 
##                      prdl.my.fctriPad1 
##                            -0.73094246 
##                      prdl.my.fctriPad2 
##                            -0.50827810 
##                      prdl.my.fctriPad3 
##                             0.09236029 
##                      prdl.my.fctriPad4 
##                             1.02122755 
##                    prdl.my.fctriPadAir 
##                             1.27964442 
##                   prdl.my.fctriPadAir2 
##                             1.73963473 
##                   prdl.my.fctriPadmini 
##                            -0.45817873 
##                  prdl.my.fctriPadmini2 
##                             1.07559291 
##                  prdl.my.fctriPadmini3 
##                             0.58081997 
##              spdiff.cut.fctr(-100,-10] 
##                             2.03718267 
##                spdiff.cut.fctr(-10,-1] 
##                             3.78016051 
##                  spdiff.cut.fctr(-1,0] 
##                             4.91900841 
##                   spdiff.cut.fctr(0,1] 
##                             4.02364901 
##                  spdiff.cut.fctr(1,10] 
##                             4.05473201 
##                spdiff.cut.fctr(10,100] 
##                             3.51590467 
##             spdiff.cut.fctr(100,1e+03] 
##                             0.83208831 
##                           sprice.root2 
##                            -0.17520239 
##                    startprice.dcm2.is9 
##                            -0.37526422 
##                    startprice.dgt1.is9 
##                            -0.12236933 
##                    startprice.dgt2.is9 
##                             0.12473613 
##                         storage.fctr16 
##                            -0.10386074 
##                         storage.fctr64 
##                             0.19776093 
##                    storage.fctrUnknown 
##                             1.37356185 
##   prdl.my.fctrUnknown:.clusterid.fctr2 
##                             0.75846077 
##     prdl.my.fctriPad1:.clusterid.fctr2 
##                            -0.47363807 
##     prdl.my.fctriPad3:.clusterid.fctr2 
##                             0.23980635 
##     prdl.my.fctriPad4:.clusterid.fctr2 
##                            -1.15493595 
##   prdl.my.fctriPadAir:.clusterid.fctr2 
##                            -0.30219482 
## prdl.my.fctriPadmini2:.clusterid.fctr2 
##                            -0.59647290 
##   prdl.my.fctrUnknown:.clusterid.fctr3 
##                            -0.71399278 
##     prdl.my.fctriPad1:.clusterid.fctr3 
##                            -0.74280799 
##     prdl.my.fctriPad4:.clusterid.fctr3 
##                            -3.99203955 
## [1] "max lambda < lambdaOpt:"
##                            (Intercept) 
##                             0.30739060 
##                                 .rnorm 
##                            -0.02307603 
##                    D.chrs.pnct11.n.log 
##                             0.41194798 
##              D.ratio.weight.sum.wrds.n 
##                            -0.11841828 
##                D.terms.post.stop.n.log 
##                            -0.04857498 
##           D.weight.sum.stem.stop.Ratio 
##                            -1.70659855 
##                      D.wrds.stop.n.log 
##                            -0.07092480 
##                               biddable 
##                             2.45597112 
##                   cellular.fctrUnknown 
##                            -1.44850328 
##                         color.fctrGold 
##                             0.06040873 
##                   color.fctrSpace Gray 
##                            -0.31733631 
##                      color.fctrUnknown 
##                             0.32580290 
##                        color.fctrWhite 
##                            -0.14400741 
## condition.fctrFor parts or not working 
##                            -0.64810440 
## condition.fctrManufacturer refurbished 
##                            -1.05310177 
##  condition.fctrNew other (see details) 
##                             0.54221686 
##       condition.fctrSeller refurbished 
##                            -0.94083418 
##                      prdl.my.fctriPad1 
##                            -0.72962954 
##                      prdl.my.fctriPad2 
##                            -0.50283877 
##                      prdl.my.fctriPad3 
##                             0.10518937 
##                      prdl.my.fctriPad4 
##                             1.05514873 
##                    prdl.my.fctriPadAir 
##                             1.31094762 
##                   prdl.my.fctriPadAir2 
##                             1.76759552 
##                   prdl.my.fctriPadmini 
##                            -0.45080046 
##                  prdl.my.fctriPadmini2 
##                             1.10294883 
##                  prdl.my.fctriPadmini3 
##                             0.60534029 
##              spdiff.cut.fctr(-100,-10] 
##                             2.07034040 
##                spdiff.cut.fctr(-10,-1] 
##                             3.81948497 
##                  spdiff.cut.fctr(-1,0] 
##                             4.97713395 
##                   spdiff.cut.fctr(0,1] 
##                             4.07618809 
##                  spdiff.cut.fctr(1,10] 
##                             4.09721806 
##                spdiff.cut.fctr(10,100] 
##                             3.55524679 
##             spdiff.cut.fctr(100,1e+03] 
##                             0.86203115 
##                           sprice.root2 
##                            -0.17628994 
##                    startprice.dcm2.is9 
##                            -0.38089328 
##                    startprice.dgt1.is9 
##                            -0.12199526 
##                    startprice.dgt2.is9 
##                             0.12986601 
##                         storage.fctr16 
##                            -0.10644192 
##                         storage.fctr64 
##                             0.19978492 
##                    storage.fctrUnknown 
##                             1.40216390 
##   prdl.my.fctrUnknown:.clusterid.fctr2 
##                             0.77627886 
##     prdl.my.fctriPad1:.clusterid.fctr2 
##                            -0.48242024 
##     prdl.my.fctriPad3:.clusterid.fctr2 
##                             0.24204519 
##     prdl.my.fctriPad4:.clusterid.fctr2 
##                            -1.18680995 
##   prdl.my.fctriPadAir:.clusterid.fctr2 
##                            -0.31435860 
## prdl.my.fctriPadmini2:.clusterid.fctr2 
##                            -0.60900002 
##   prdl.my.fctrUnknown:.clusterid.fctr3 
##                            -0.72474486 
##     prdl.my.fctriPad1:.clusterid.fctr3 
##                            -0.75028690 
##     prdl.my.fctriPad4:.clusterid.fctr3 
##                            -4.10529031

##   sold.fctr sold.fctr.predict.Low.cor.X.glmnet.N
## 1         N                                  493
## 2         Y                                   85
##   sold.fctr.predict.Low.cor.X.glmnet.Y
## 1                                   55
## 2                                  387
##          Prediction
## Reference   N   Y
##         N 493  55
##         Y  85 387
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   8.627451e-01   7.227357e-01   8.400893e-01   8.832827e-01   5.372549e-01 
## AccuracyPValue  McnemarPValue 
##  3.342931e-109   1.424808e-02

##   sold.fctr sold.fctr.predict.Low.cor.X.glmnet.N
## 1         N                                  313
## 2         Y                                   58
##   sold.fctr.predict.Low.cor.X.glmnet.Y
## 1                                  134
## 2                                  325
##          Prediction
## Reference   N   Y
##         N 313 134
##         Y  58 325
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.686747e-01   5.411011e-01   7.384586e-01   7.969687e-01   5.385542e-01 
## AccuracyPValue  McnemarPValue 
##   5.056117e-43   6.209575e-08 
##                 id
## 1 Low.cor.X.glmnet
##                                                                                                                                                                                                                                                                                                                          feats
## 1 biddable,spdiff.cut.fctr,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,storage.fctr,D.chrs.pnct11.n.log,startprice.dgt2.is9,color.fctr,prdl.my.fctr,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.root2,prdl.my.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                      3.129                 0.121
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.8597751     0.899635    0.8199153       0.9329264
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.5       0.8468271        0.8349657
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.8400893             0.8832827     0.6666179
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.7801532    0.8187919    0.7415144       0.8727052
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.3       0.7719715        0.7686747
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.7384586             0.7969687     0.5411011
##   max.AccuracySD.fit max.KappaSD.fit
## 1        0.009378705      0.01938402
rm(ret_lst)

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)
##         label step_major step_minor label_minor     bgn     end elapsed
## 10 fit.models          6          0           0 342.849 409.618  66.769
## 11 fit.models          6          1           1 409.619      NA      NA
fit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor="setup")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_1_bgn          1          0       setup 423.992  NA      NA
#stop(here"); glb_to_sav(); all.equal(glb_models_df, sav_models_df)
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glb_mdl_family_lst)) {
    fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, 
        paste0("fit.models_1_", mdl_id_pfx), major.inc = TRUE, label.minor = "setup")

    indep_vars <- NULL;

    if (grepl("\\.Interact", mdl_id_pfx)) {
        if (is.null(topindep_var) && is.null(interact_vars)) {
        #   select best glmnet model upto now
            dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
                                     glb_models_df)
            dsp_models_df <- subset(dsp_models_df, grepl(".glmnet", id, fixed=TRUE))
            bst_mdl_id <- dsp_models_df$id[1]
            mdl_id_pfx <- 
                paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
                      collapse=".")
        #   select most importance feature
            if (is.null(bst_featsimp_df <- 
                        myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
                warning("Base model for RFE.Interact: ", bst_mdl_id, 
                        " has no important features")
                next
            }    
            
            topindep_ix <- 1
            while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
                topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
                if (grepl(".fctr", topindep_var, fixed=TRUE))
                    topindep_var <- 
                        paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
                if (topindep_var %in% names(glb_interaction_only_feats_lst)) {
                    topindep_var <- NULL; topindep_ix <- topindep_ix + 1
                } else break
            }
            
        #   select features with importance > max(10, importance of .rnorm) & is not highest
        #       combine factor dummy features to just the factor feature
            if (length(pos_rnorm <- 
                       grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
                imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
                imp_rnorm <- NA    
            importance_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
            interact_vars <- 
                tail(row.names(subset(bst_featsimp_df, 
                                      importance > importance_cutoff)), -1)
            if (length(interact_vars) > 0) {
                interact_vars <-
                    myadjust_interaction_feats(myextract_actual_feats(interact_vars))
                interact_vars <- 
                    interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
            }
            ### bid0_sp only
#             interact_vars <- c(
#     "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
#     "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
#     "D.chrs.n.log", "color.fctr"
#     # , "condition.fctr", "prdl.my.descr.fctr"
#                                 )
#            interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
            ###
            indep_vars <- myextract_actual_feats(row.names(bst_featsimp_df))
            indep_vars <- setdiff(indep_vars, topindep_var)
            if (length(interact_vars) > 0) {
                indep_vars <- 
                    setdiff(indep_vars, myextract_actual_feats(interact_vars))
                indep_vars <- c(indep_vars, 
                    paste(topindep_var, setdiff(interact_vars, topindep_var), 
                          sep = "*"))
            } else indep_vars <- union(indep_vars, topindep_var)
        }
    }
    
    if (is.null(indep_vars))
        indep_vars <- glb_mdl_feats_lst[[mdl_id_pfx]]
    
    if (is.null(indep_vars) && grepl("RFE\\.", mdl_id_pfx))
        indep_vars <- myextract_actual_feats(predictors(rfe_fit_results))
    
    if (is.null(indep_vars))
        indep_vars <- subset(glb_feats_df, !nzv & (exclude.as.feat != 1))[, "id"]
        
    indep_vars <- myadjust_interaction_feats(indep_vars)
    
    if (grepl("\\.Interact", mdl_id_pfx)) { 
        # if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
        if (is.null(glb_mdl_family_lst[[mdl_id_pfx]])) {
            if (!is.null(glb_mdl_family_lst[["Best.Interact"]]))
                glb_mdl_family_lst[[mdl_id_pfx]] <-
                    glb_mdl_family_lst[["Best.Interact"]]
        }
    }
    
    if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
        fitobs_df <- glb_fitobs_df[!(glb_fitobs_df[, glb_id_var] %in%
                                         glbObsFitOutliers[[mdl_id_pfx]]), ]
    } else fitobs_df <- glb_fitobs_df

    if (is.null(glb_mdl_family_lst[[mdl_id_pfx]]))
        mdl_methods <- glbMdlMethods else
        mdl_methods <- glb_mdl_family_lst[[mdl_id_pfx]]    

    for (method in mdl_methods) {
        if (method %in% c("rpart", "rf")) {
            # rpart:    fubar's the tree
            # rf:       skip the scenario w/ .rnorm for speed
            indep_vars <- setdiff(indep_vars, c(".rnorm"))
            #mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
        } 

        fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, 
                            paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
                                    label.minor = method)
        ret_lst <- 
            myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = mdl_id_pfx, 
            type = glb_model_type, tune.df = glb_tune_models_df,
            trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds,
            trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            #trainControl.allowParallel = FALSE,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = method)),
            indep_vars = indep_vars, rsp_var = glb_rsp_var, 
            fit_df = fitobs_df, OOB_df = glb_OOBobs_df)
    }
}
##                label step_major step_minor label_minor     bgn     end
## 1   fit.models_1_bgn          1          0       setup 423.992 424.003
## 2 fit.models_1_RFE.X          2          0       setup 424.003      NA
##   elapsed
## 1   0.011
## 2      NA
##                label step_major step_minor label_minor     bgn     end
## 2 fit.models_1_RFE.X          2          0       setup 424.003 424.011
## 3 fit.models_1_RFE.X          2          1      glmnet 424.011      NA
##   elapsed
## 2   0.008
## 3      NA
## [1] "fitting model: RFE.X.glmnet"
## [1] "    indep_vars: sprice.d20nexp,sprice.log10,sprice.root2,biddable,spdiff.cut.fctr,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,D.terms.post.stem.n.log,D.chrs.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.000267 on full training set
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0           100   -none-     numeric  
## beta        9300   dgCMatrix  S4       
## df           100   -none-     numeric  
## dim            2   -none-     numeric  
## lambda       100   -none-     numeric  
## dev.ratio    100   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## classnames     2   -none-     character
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        93   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      2   -none-     character
## [1] "min lambda > lambdaOpt:"
##                              (Intercept) 
##                             5.9298599950 
##                                   .rnorm 
##                            -0.0367487387 
##                             D.chrs.n.log 
##                            -0.2852958123 
##                      D.chrs.pnct11.n.log 
##                             0.4714450805 
##                      D.chrs.pnct13.n.log 
##                            -0.1653784579 
##                        D.chrs.uppr.n.log 
##                            -0.0116026565 
##                D.ratio.weight.sum.wrds.n 
##                            -0.0593199934 
##               D.ratio.wrds.stop.n.wrds.n 
##                            -2.9071388332 
##                  D.terms.post.stem.n.log 
##                            -0.2864766681 
##                  D.terms.post.stop.n.log 
##                            -0.9265313808 
##                   D.weight.post.stem.sum 
##                            -0.1209650771 
##                             D.weight.sum 
##                            -0.1180951219 
##             D.weight.sum.stem.stop.Ratio 
##                            -2.9013605803 
##                             D.wrds.n.log 
##                             1.2607015439 
##                        D.wrds.stop.n.log 
##                             0.1152103538 
##                         D.wrds.unq.n.log 
##                            -0.1770678274 
##                                 biddable 
##                             2.5343114368 
##                     cellular.fctrUnknown 
##                            -0.7922522457 
##                           color.fctrGold 
##                             0.0871372337 
##                     color.fctrSpace Gray 
##                            -0.3474018561 
##                        color.fctrUnknown 
##                             0.3560862355 
##                          color.fctrWhite 
##                            -0.2253657549 
##   condition.fctrFor parts or not working 
##                            -0.7365978083 
##   condition.fctrManufacturer refurbished 
##                            -1.1969859100 
##                        condition.fctrNew 
##                             0.0297417566 
##    condition.fctrNew other (see details) 
##                             0.7229925582 
##         condition.fctrSeller refurbished 
##                            -1.0271638293 
##                        prdl.my.fctriPad1 
##                            -0.6856562063 
##                        prdl.my.fctriPad2 
##                            -0.4622200902 
##                        prdl.my.fctriPad3 
##                             0.1837795731 
##                        prdl.my.fctriPad4 
##                             1.4053112779 
##                      prdl.my.fctriPadAir 
##                             1.5486082406 
##                     prdl.my.fctriPadAir2 
##                             1.9064724435 
##                     prdl.my.fctriPadmini 
##                            -0.4755225796 
##                    prdl.my.fctriPadmini2 
##                             1.3324979842 
##                    prdl.my.fctriPadmini3 
##                             0.7573021470 
##                spdiff.cut.fctr(-100,-10] 
##                             2.3577551693 
##                  spdiff.cut.fctr(-10,-1] 
##                             4.1547129846 
##                    spdiff.cut.fctr(-1,0] 
##                             5.3348860714 
##                     spdiff.cut.fctr(0,1] 
##                             4.4072812301 
##                    spdiff.cut.fctr(1,10] 
##                             4.4073453219 
##                  spdiff.cut.fctr(10,100] 
##                             3.8533894456 
##               spdiff.cut.fctr(100,1e+03] 
##                             1.0918155713 
##                           sprice.d20nexp 
##                            -1.5657841541 
##                             sprice.log10 
##                            -0.5769819282 
##                             sprice.root2 
##                            -0.1085201562 
##                      startprice.dcm1.is9 
##                            -0.4894970169 
##                      startprice.dcm2.is9 
##                            -0.0838883551 
##                      startprice.dgt1.is9 
##                             0.0254181773 
##                      startprice.dgt2.is9 
##                             0.1157259996 
##                           storage.fctr16 
##                            -0.1300313626 
##                           storage.fctr32 
##                             0.0005589105 
##                           storage.fctr64 
##                             0.2180851783 
##                      storage.fctrUnknown 
##                             1.7336441556 
##          cellular.fctr0:carrier.fctrNone 
##                             0.1706810167 
##         cellular.fctr1:carrier.fctrOther 
##                             2.6072585524 
##        cellular.fctr1:carrier.fctrSprint 
##                             1.0270604082 
##      cellular.fctr1:carrier.fctrT-Mobile 
##                            -0.2821763037 
##       cellular.fctr1:carrier.fctrUnknown 
##                            -0.0179172973 
## cellular.fctrUnknown:carrier.fctrUnknown 
##                            -0.7883348835 
##       cellular.fctr1:carrier.fctrVerizon 
##                             0.3156224380 
##     prdl.my.fctrUnknown:.clusterid.fctr2 
##                             0.8932344300 
##       prdl.my.fctriPad1:.clusterid.fctr2 
##                            -0.7404516345 
##       prdl.my.fctriPad3:.clusterid.fctr2 
##                             0.2701330403 
##       prdl.my.fctriPad4:.clusterid.fctr2 
##                            -1.4552193885 
##     prdl.my.fctriPadAir:.clusterid.fctr2 
##                            -0.3279730199 
##   prdl.my.fctriPadmini2:.clusterid.fctr2 
##                            -0.6287767988 
##     prdl.my.fctrUnknown:.clusterid.fctr3 
##                            -0.7598398151 
##       prdl.my.fctriPad1:.clusterid.fctr3 
##                            -1.1337789530 
##       prdl.my.fctriPad4:.clusterid.fctr3 
##                            -5.9466674332 
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
##  [1] "(Intercept)"                              
##  [2] ".rnorm"                                   
##  [3] "D.chrs.n.log"                             
##  [4] "D.chrs.pnct11.n.log"                      
##  [5] "D.chrs.pnct13.n.log"                      
##  [6] "D.chrs.uppr.n.log"                        
##  [7] "D.ratio.weight.sum.wrds.n"                
##  [8] "D.ratio.wrds.stop.n.wrds.n"               
##  [9] "D.terms.post.stem.n.log"                  
## [10] "D.terms.post.stop.n.log"                  
## [11] "D.weight.post.stem.sum"                   
## [12] "D.weight.post.stop.sum"                   
## [13] "D.weight.sum"                             
## [14] "D.weight.sum.stem.stop.Ratio"             
## [15] "D.wrds.n.log"                             
## [16] "D.wrds.stop.n.log"                        
## [17] "D.wrds.unq.n.log"                         
## [18] "biddable"                                 
## [19] "cellular.fctr1"                           
## [20] "cellular.fctrUnknown"                     
## [21] "color.fctrGold"                           
## [22] "color.fctrSpace Gray"                     
## [23] "color.fctrUnknown"                        
## [24] "color.fctrWhite"                          
## [25] "condition.fctrFor parts or not working"   
## [26] "condition.fctrManufacturer refurbished"   
## [27] "condition.fctrNew"                        
## [28] "condition.fctrNew other (see details)"    
## [29] "condition.fctrSeller refurbished"         
## [30] "prdl.my.fctriPad1"                        
## [31] "prdl.my.fctriPad2"                        
## [32] "prdl.my.fctriPad3"                        
## [33] "prdl.my.fctriPad4"                        
## [34] "prdl.my.fctriPadAir"                      
## [35] "prdl.my.fctriPadAir2"                     
## [36] "prdl.my.fctriPadmini"                     
## [37] "prdl.my.fctriPadmini2"                    
## [38] "prdl.my.fctriPadmini3"                    
## [39] "spdiff.cut.fctr(-100,-10]"                
## [40] "spdiff.cut.fctr(-10,-1]"                  
## [41] "spdiff.cut.fctr(-1,0]"                    
## [42] "spdiff.cut.fctr(0,1]"                     
## [43] "spdiff.cut.fctr(1,10]"                    
## [44] "spdiff.cut.fctr(10,100]"                  
## [45] "spdiff.cut.fctr(100,1e+03]"               
## [46] "sprice.d20nexp"                           
## [47] "sprice.log10"                             
## [48] "sprice.root2"                             
## [49] "startprice.dcm1.is9"                      
## [50] "startprice.dcm2.is9"                      
## [51] "startprice.dgt1.is9"                      
## [52] "startprice.dgt2.is9"                      
## [53] "storage.fctr16"                           
## [54] "storage.fctr32"                           
## [55] "storage.fctr64"                           
## [56] "storage.fctrUnknown"                      
## [57] "cellular.fctr0:carrier.fctrNone"          
## [58] "cellular.fctr1:carrier.fctrNone"          
## [59] "cellular.fctrUnknown:carrier.fctrNone"    
## [60] "cellular.fctr0:carrier.fctrOther"         
## [61] "cellular.fctr1:carrier.fctrOther"         
## [62] "cellular.fctrUnknown:carrier.fctrOther"   
## [63] "cellular.fctr0:carrier.fctrSprint"        
## [64] "cellular.fctr1:carrier.fctrSprint"        
## [65] "cellular.fctrUnknown:carrier.fctrSprint"  
## [66] "cellular.fctr0:carrier.fctrT-Mobile"      
## [67] "cellular.fctr1:carrier.fctrT-Mobile"      
## [68] "cellular.fctrUnknown:carrier.fctrT-Mobile"
## [69] "cellular.fctr0:carrier.fctrUnknown"       
## [70] "cellular.fctr1:carrier.fctrUnknown"       
## [71] "cellular.fctrUnknown:carrier.fctrUnknown" 
## [72] "cellular.fctr0:carrier.fctrVerizon"       
## [73] "cellular.fctr1:carrier.fctrVerizon"       
## [74] "cellular.fctrUnknown:carrier.fctrVerizon" 
## [75] "prdl.my.fctrUnknown:.clusterid.fctr2"     
## [76] "prdl.my.fctriPad1:.clusterid.fctr2"       
## [77] "prdl.my.fctriPad2:.clusterid.fctr2"       
## [78] "prdl.my.fctriPad3:.clusterid.fctr2"       
## [79] "prdl.my.fctriPad4:.clusterid.fctr2"       
## [80] "prdl.my.fctriPadAir:.clusterid.fctr2"     
## [81] "prdl.my.fctriPadAir2:.clusterid.fctr2"    
## [82] "prdl.my.fctriPadmini:.clusterid.fctr2"    
## [83] "prdl.my.fctriPadmini2:.clusterid.fctr2"   
## [84] "prdl.my.fctriPadmini3:.clusterid.fctr2"   
## [85] "prdl.my.fctrUnknown:.clusterid.fctr3"     
## [86] "prdl.my.fctriPad1:.clusterid.fctr3"       
## [87] "prdl.my.fctriPad2:.clusterid.fctr3"       
## [88] "prdl.my.fctriPad3:.clusterid.fctr3"       
## [89] "prdl.my.fctriPad4:.clusterid.fctr3"       
## [90] "prdl.my.fctriPadAir:.clusterid.fctr3"     
## [91] "prdl.my.fctriPadAir2:.clusterid.fctr3"    
## [92] "prdl.my.fctriPadmini:.clusterid.fctr3"    
## [93] "prdl.my.fctriPadmini2:.clusterid.fctr3"   
## [94] "prdl.my.fctriPadmini3:.clusterid.fctr3"

##   sold.fctr sold.fctr.predict.RFE.X.glmnet.N
## 1         N                              467
## 2         Y                               63
##   sold.fctr.predict.RFE.X.glmnet.Y
## 1                               81
## 2                              409
##          Prediction
## Reference   N   Y
##         N 467  81
##         Y  63 409
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   8.588235e-01   7.168196e-01   8.359295e-01   8.796230e-01   5.372549e-01 
## AccuracyPValue  McnemarPValue 
##  2.677951e-106   1.565804e-01

##   sold.fctr sold.fctr.predict.RFE.X.glmnet.N
## 1         N                              309
## 2         Y                               60
##   sold.fctr.predict.RFE.X.glmnet.Y
## 1                              138
## 2                              323
##          Prediction
## Reference   N   Y
##         N 309 138
##         Y  60 323
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.614458e-01   5.269348e-01   7.309494e-01   7.900724e-01   5.385542e-01 
## AccuracyPValue  McnemarPValue 
##   2.413828e-40   4.446039e-08 
##             id
## 1 RFE.X.glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    feats
## 1 sprice.d20nexp,sprice.log10,sprice.root2,biddable,spdiff.cut.fctr,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,D.terms.post.stem.n.log,D.chrs.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     15.219                 0.534
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.8626593    0.9032847    0.8220339       0.9355785
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.4       0.8503119        0.8303886
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.8359295              0.879623     0.6576319
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.7835089    0.8255034    0.7415144       0.8721444
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.3       0.7654028        0.7614458
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.7309494             0.7900724     0.5269348
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01126649      0.02276618
##                label step_major step_minor label_minor     bgn     end
## 3 fit.models_1_RFE.X          2          1      glmnet 424.011 443.927
## 4 fit.models_1_RFE.X          2          2         glm 443.928      NA
##   elapsed
## 3  19.916
## 4      NA
## [1] "fitting model: RFE.X.glm"
## [1] "    indep_vars: sprice.d20nexp,sprice.log10,sprice.root2,biddable,spdiff.cut.fctr,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,D.terms.post.stem.n.log,D.chrs.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## + Fold1.Rep1: parameter=none
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold1.Rep1: parameter=none 
## + Fold2.Rep1: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold2.Rep1: parameter=none 
## + Fold3.Rep1: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3.Rep1: parameter=none 
## + Fold1.Rep2: parameter=none
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold1.Rep2: parameter=none 
## + Fold2.Rep2: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold2.Rep2: parameter=none 
## + Fold3.Rep2: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3.Rep2: parameter=none 
## + Fold1.Rep3: parameter=none
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold1.Rep3: parameter=none 
## + Fold2.Rep3: parameter=none
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold2.Rep3: parameter=none 
## + Fold3.Rep3: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3.Rep3: parameter=none 
## Aggregating results
## Fitting final model on full training set
## Warning: not plotting observations with leverage one:
##   683

## Warning: not plotting observations with leverage one:
##   683

## 
## Call:
## NULL
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.8799  -0.4583  -0.0814   0.3151   3.4426  
## 
## Coefficients: (26 not defined because of singularities)
##                                               Estimate Std. Error z value
## (Intercept)                                 -2.401e+01  3.068e+01  -0.783
## .rnorm                                      -1.788e-02  1.057e-01  -0.169
## D.chrs.n.log                                -6.596e+00  4.632e+00  -1.424
## D.chrs.pnct11.n.log                          6.989e-01  4.210e-01   1.660
## D.chrs.pnct13.n.log                          1.283e-01  4.155e-01   0.309
## D.chrs.uppr.n.log                            4.347e+00  4.277e+00   1.016
## D.ratio.weight.sum.wrds.n                    2.759e-01  3.276e-01   0.842
## D.ratio.wrds.stop.n.wrds.n                  -1.040e+01  4.111e+00  -2.530
## D.terms.post.stem.n.log                      2.354e+01  1.124e+01   2.094
## D.terms.post.stop.n.log                     -3.028e+01  1.119e+01  -2.706
## D.weight.post.stem.sum                      -7.805e+00  5.538e+00  -1.409
## D.weight.post.stop.sum                       7.281e+00  5.343e+00   1.363
## D.weight.sum                                        NA         NA      NA
## D.weight.sum.stem.stop.Ratio                 3.529e+01  3.102e+01   1.138
## D.wrds.n.log                                 7.889e+00  2.575e+00   3.064
## D.wrds.stop.n.log                            2.840e-01  7.047e-01   0.403
## D.wrds.unq.n.log                                    NA         NA      NA
## biddable                                     2.630e+00  2.970e-01   8.856
## cellular.fctr1                              -2.086e-01  3.623e-01  -0.576
## cellular.fctrUnknown                        -1.826e+00  6.228e-01  -2.931
## color.fctrGold                               9.378e-02  7.237e-01   0.130
## `color.fctrSpace Gray`                      -3.219e-01  4.232e-01  -0.761
## color.fctrUnknown                            3.371e-01  2.968e-01   1.136
## color.fctrWhite                             -1.613e-01  3.303e-01  -0.488
## `condition.fctrFor parts or not working`    -9.316e-01  4.578e-01  -2.035
## `condition.fctrManufacturer refurbished`    -1.304e+00  8.416e-01  -1.549
## condition.fctrNew                            5.179e-02  4.037e-01   0.128
## `condition.fctrNew other (see details)`      6.535e-01  5.671e-01   1.152
## `condition.fctrSeller refurbished`          -1.092e+00  4.754e-01  -2.296
## prdl.my.fctriPad1                           -6.632e-01  6.929e-01  -0.957
## prdl.my.fctriPad2                           -4.322e-01  6.111e-01  -0.707
## prdl.my.fctriPad3                            2.127e-01  6.437e-01   0.330
## prdl.my.fctriPad4                            1.449e+00  7.149e-01   2.027
## prdl.my.fctriPadAir                          1.646e+00  7.081e-01   2.324
## prdl.my.fctriPadAir2                         1.802e+00  7.860e-01   2.293
## prdl.my.fctriPadmini                        -6.314e-01  5.989e-01  -1.054
## prdl.my.fctriPadmini2                        1.177e+00  7.368e-01   1.597
## prdl.my.fctriPadmini3                        5.642e-01  8.039e-01   0.702
## `spdiff.cut.fctr(-100,-10]`                  2.478e+00  4.231e-01   5.857
## `spdiff.cut.fctr(-10,-1]`                    4.362e+00  5.586e-01   7.809
## `spdiff.cut.fctr(-1,0]`                      5.453e+00  1.021e+00   5.339
## `spdiff.cut.fctr(0,1]`                       4.625e+00  1.319e+00   3.508
## `spdiff.cut.fctr(1,10]`                      4.774e+00  5.632e-01   8.476
## `spdiff.cut.fctr(10,100]`                    4.088e+00  5.065e-01   8.071
## `spdiff.cut.fctr(100,1e+03]`                 1.217e+00  8.025e-01   1.517
## sprice.d20nexp                              -2.326e+00  3.455e+00  -0.673
## sprice.log10                                -8.826e-01  1.088e+00  -0.811
## sprice.root2                                -5.536e-02  1.602e-01  -0.345
## startprice.dcm1.is9                         -5.127e-01  4.195e-01  -1.222
## startprice.dcm2.is9                         -5.052e-02  4.086e-01  -0.124
## startprice.dgt1.is9                         -1.686e-02  2.977e-01  -0.057
## startprice.dgt2.is9                          6.216e-02  3.276e-01   0.190
## storage.fctr16                              -8.848e-03  7.434e-01  -0.012
## storage.fctr32                               1.539e-01  7.550e-01   0.204
## storage.fctr64                               2.955e-01  7.377e-01   0.401
## storage.fctrUnknown                          1.900e+00  9.548e-01   1.990
## `cellular.fctr0:carrier.fctrNone`                   NA         NA      NA
## `cellular.fctr1:carrier.fctrNone`                   NA         NA      NA
## `cellular.fctrUnknown:carrier.fctrNone`             NA         NA      NA
## `cellular.fctr0:carrier.fctrOther`                  NA         NA      NA
## `cellular.fctr1:carrier.fctrOther`           1.273e+01  2.400e+03   0.005
## `cellular.fctrUnknown:carrier.fctrOther`            NA         NA      NA
## `cellular.fctr0:carrier.fctrSprint`                 NA         NA      NA
## `cellular.fctr1:carrier.fctrSprint`          9.312e-01  7.822e-01   1.190
## `cellular.fctrUnknown:carrier.fctrSprint`           NA         NA      NA
## `cellular.fctr0:carrier.fctrT-Mobile`               NA         NA      NA
## `cellular.fctr1:carrier.fctrT-Mobile`        2.874e-02  1.283e+00   0.022
## `cellular.fctrUnknown:carrier.fctrT-Mobile`         NA         NA      NA
## `cellular.fctr0:carrier.fctrUnknown`                NA         NA      NA
## `cellular.fctr1:carrier.fctrUnknown`        -6.309e-03  6.176e-01  -0.010
## `cellular.fctrUnknown:carrier.fctrUnknown`          NA         NA      NA
## `cellular.fctr0:carrier.fctrVerizon`                NA         NA      NA
## `cellular.fctr1:carrier.fctrVerizon`         4.164e-01  5.119e-01   0.813
## `cellular.fctrUnknown:carrier.fctrVerizon`          NA         NA      NA
## `prdl.my.fctrUnknown:.clusterid.fctr2`       6.062e-01  8.836e-01   0.686
## `prdl.my.fctriPad1:.clusterid.fctr2`        -1.344e+00  8.787e-01  -1.529
## `prdl.my.fctriPad2:.clusterid.fctr2`                NA         NA      NA
## `prdl.my.fctriPad3:.clusterid.fctr2`         6.740e-02  9.757e-01   0.069
## `prdl.my.fctriPad4:.clusterid.fctr2`        -1.954e+00  1.355e+00  -1.442
## `prdl.my.fctriPadAir:.clusterid.fctr2`      -3.340e-01  9.500e-01  -0.352
## `prdl.my.fctriPadAir2:.clusterid.fctr2`             NA         NA      NA
## `prdl.my.fctriPadmini:.clusterid.fctr2`             NA         NA      NA
## `prdl.my.fctriPadmini2:.clusterid.fctr2`    -6.927e-01  1.494e+00  -0.464
## `prdl.my.fctriPadmini3:.clusterid.fctr2`            NA         NA      NA
## `prdl.my.fctrUnknown:.clusterid.fctr3`      -7.640e-02  1.280e+00  -0.060
## `prdl.my.fctriPad1:.clusterid.fctr3`        -1.353e+00  8.714e-01  -1.553
## `prdl.my.fctriPad2:.clusterid.fctr3`                NA         NA      NA
## `prdl.my.fctriPad3:.clusterid.fctr3`                NA         NA      NA
## `prdl.my.fctriPad4:.clusterid.fctr3`        -1.652e+01  4.931e+02  -0.034
## `prdl.my.fctriPadAir:.clusterid.fctr3`              NA         NA      NA
## `prdl.my.fctriPadAir2:.clusterid.fctr3`             NA         NA      NA
## `prdl.my.fctriPadmini:.clusterid.fctr3`             NA         NA      NA
## `prdl.my.fctriPadmini2:.clusterid.fctr3`            NA         NA      NA
## `prdl.my.fctriPadmini3:.clusterid.fctr3`            NA         NA      NA
##                                             Pr(>|z|)    
## (Intercept)                                 0.433733    
## .rnorm                                      0.865713    
## D.chrs.n.log                                0.154373    
## D.chrs.pnct11.n.log                         0.096884 .  
## D.chrs.pnct13.n.log                         0.757516    
## D.chrs.uppr.n.log                           0.309512    
## D.ratio.weight.sum.wrds.n                   0.399691    
## D.ratio.wrds.stop.n.wrds.n                  0.011396 *  
## D.terms.post.stem.n.log                     0.036258 *  
## D.terms.post.stop.n.log                     0.006800 ** 
## D.weight.post.stem.sum                      0.158704    
## D.weight.post.stop.sum                      0.172972    
## D.weight.sum                                      NA    
## D.weight.sum.stem.stop.Ratio                0.255188    
## D.wrds.n.log                                0.002185 ** 
## D.wrds.stop.n.log                           0.686961    
## D.wrds.unq.n.log                                  NA    
## biddable                                     < 2e-16 ***
## cellular.fctr1                              0.564862    
## cellular.fctrUnknown                        0.003376 ** 
## color.fctrGold                              0.896906    
## `color.fctrSpace Gray`                      0.446886    
## color.fctrUnknown                           0.256140    
## color.fctrWhite                             0.625236    
## `condition.fctrFor parts or not working`    0.041845 *  
## `condition.fctrManufacturer refurbished`    0.121369    
## condition.fctrNew                           0.897934    
## `condition.fctrNew other (see details)`     0.249218    
## `condition.fctrSeller refurbished`          0.021667 *  
## prdl.my.fctriPad1                           0.338494    
## prdl.my.fctriPad2                           0.479456    
## prdl.my.fctriPad3                           0.741110    
## prdl.my.fctriPad4                           0.042655 *  
## prdl.my.fctriPadAir                         0.020109 *  
## prdl.my.fctriPadAir2                        0.021849 *  
## prdl.my.fctriPadmini                        0.291796    
## prdl.my.fctriPadmini2                       0.110192    
## prdl.my.fctriPadmini3                       0.482795    
## `spdiff.cut.fctr(-100,-10]`                 4.71e-09 ***
## `spdiff.cut.fctr(-10,-1]`                   5.76e-15 ***
## `spdiff.cut.fctr(-1,0]`                     9.34e-08 ***
## `spdiff.cut.fctr(0,1]`                      0.000452 ***
## `spdiff.cut.fctr(1,10]`                      < 2e-16 ***
## `spdiff.cut.fctr(10,100]`                   6.99e-16 ***
## `spdiff.cut.fctr(100,1e+03]`                0.129375    
## sprice.d20nexp                              0.500735    
## sprice.log10                                0.417449    
## sprice.root2                                0.729739    
## startprice.dcm1.is9                         0.221611    
## startprice.dcm2.is9                         0.901608    
## startprice.dgt1.is9                         0.954845    
## startprice.dgt2.is9                         0.849498    
## storage.fctr16                              0.990505    
## storage.fctr32                              0.838496    
## storage.fctr64                              0.688748    
## storage.fctrUnknown                         0.046572 *  
## `cellular.fctr0:carrier.fctrNone`                 NA    
## `cellular.fctr1:carrier.fctrNone`                 NA    
## `cellular.fctrUnknown:carrier.fctrNone`           NA    
## `cellular.fctr0:carrier.fctrOther`                NA    
## `cellular.fctr1:carrier.fctrOther`          0.995769    
## `cellular.fctrUnknown:carrier.fctrOther`          NA    
## `cellular.fctr0:carrier.fctrSprint`               NA    
## `cellular.fctr1:carrier.fctrSprint`         0.233863    
## `cellular.fctrUnknown:carrier.fctrSprint`         NA    
## `cellular.fctr0:carrier.fctrT-Mobile`             NA    
## `cellular.fctr1:carrier.fctrT-Mobile`       0.982129    
## `cellular.fctrUnknown:carrier.fctrT-Mobile`       NA    
## `cellular.fctr0:carrier.fctrUnknown`              NA    
## `cellular.fctr1:carrier.fctrUnknown`        0.991849    
## `cellular.fctrUnknown:carrier.fctrUnknown`        NA    
## `cellular.fctr0:carrier.fctrVerizon`              NA    
## `cellular.fctr1:carrier.fctrVerizon`        0.416013    
## `cellular.fctrUnknown:carrier.fctrVerizon`        NA    
## `prdl.my.fctrUnknown:.clusterid.fctr2`      0.492623    
## `prdl.my.fctriPad1:.clusterid.fctr2`        0.126225    
## `prdl.my.fctriPad2:.clusterid.fctr2`              NA    
## `prdl.my.fctriPad3:.clusterid.fctr2`        0.944930    
## `prdl.my.fctriPad4:.clusterid.fctr2`        0.149436    
## `prdl.my.fctriPadAir:.clusterid.fctr2`      0.725134    
## `prdl.my.fctriPadAir2:.clusterid.fctr2`           NA    
## `prdl.my.fctriPadmini:.clusterid.fctr2`           NA    
## `prdl.my.fctriPadmini2:.clusterid.fctr2`    0.642927    
## `prdl.my.fctriPadmini3:.clusterid.fctr2`          NA    
## `prdl.my.fctrUnknown:.clusterid.fctr3`      0.952415    
## `prdl.my.fctriPad1:.clusterid.fctr3`        0.120433    
## `prdl.my.fctriPad2:.clusterid.fctr3`              NA    
## `prdl.my.fctriPad3:.clusterid.fctr3`              NA    
## `prdl.my.fctriPad4:.clusterid.fctr3`        0.973270    
## `prdl.my.fctriPadAir:.clusterid.fctr3`            NA    
## `prdl.my.fctriPadAir2:.clusterid.fctr3`           NA    
## `prdl.my.fctriPadmini:.clusterid.fctr3`           NA    
## `prdl.my.fctriPadmini2:.clusterid.fctr3`          NA    
## `prdl.my.fctriPadmini3:.clusterid.fctr3`          NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1408.35  on 1019  degrees of freedom
## Residual deviance:  633.39  on  952  degrees of freedom
## AIC: 769.39
## 
## Number of Fisher Scoring iterations: 15
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

##   sold.fctr sold.fctr.predict.RFE.X.glm.N sold.fctr.predict.RFE.X.glm.Y
## 1         N                           469                            79
## 2         Y                            65                           407
##          Prediction
## Reference   N   Y
##         N 469  79
##         Y  65 407
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   8.588235e-01   7.166535e-01   8.359295e-01   8.796230e-01   5.372549e-01 
## AccuracyPValue  McnemarPValue 
##  2.677951e-106   2.786605e-01
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

##   sold.fctr sold.fctr.predict.RFE.X.glm.N sold.fctr.predict.RFE.X.glm.Y
## 1         N                           367                            80
## 2         Y                            96                           287
##          Prediction
## Reference   N   Y
##         N 367  80
##         Y  96 287
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.879518e-01   5.720872e-01   7.585420e-01   8.152999e-01   5.385542e-01 
## AccuracyPValue  McnemarPValue 
##   1.092681e-50   2.581950e-01 
##          id
## 1 RFE.X.glm
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    feats
## 1 sprice.d20nexp,sprice.log10,sprice.root2,biddable,spdiff.cut.fctr,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,D.terms.post.stem.n.log,D.chrs.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1               1                       2.89                 0.192
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.8597751     0.899635    0.8199153       0.9392823
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.4       0.8496868        0.8307135
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.8359295              0.879623     0.6585198
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.7851882    0.8210291    0.7493473       0.8700475
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.5       0.7653333        0.7879518
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1              0.758542             0.8152999     0.5720872
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01447782      0.02900194
##                label step_major step_minor label_minor     bgn     end
## 4 fit.models_1_RFE.X          2          2         glm 443.928 451.375
## 5 fit.models_1_All.X          3          0       setup 451.376      NA
##   elapsed
## 4   7.447
## 5      NA
##                label step_major step_minor label_minor     bgn     end
## 5 fit.models_1_All.X          3          0       setup 451.376 451.383
## 6 fit.models_1_All.X          3          1      glmnet 451.384      NA
##   elapsed
## 5   0.007
## 6      NA
## [1] "fitting model: All.X.glmnet"
## [1] "    indep_vars: biddable,sprice.d20nexp,spdiff.cut.fctr,D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,startprice.dcm1.is9,storage.fctr,D.chrs.pnct11.n.log,D.chrs.pnct13.n.log,startprice.dgt2.is9,color.fctr,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,prdl.my.fctr,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.log10,sprice.root2,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.000267 on full training set
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0           100   -none-     numeric  
## beta        9300   dgCMatrix  S4       
## df           100   -none-     numeric  
## dim            2   -none-     numeric  
## lambda       100   -none-     numeric  
## dev.ratio    100   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## classnames     2   -none-     character
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        93   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      2   -none-     character
## [1] "min lambda > lambdaOpt:"
##                              (Intercept) 
##                             5.9298599950 
##                                   .rnorm 
##                            -0.0367487387 
##                             D.chrs.n.log 
##                            -0.2852958123 
##                      D.chrs.pnct11.n.log 
##                             0.4714450805 
##                      D.chrs.pnct13.n.log 
##                            -0.1653784579 
##                        D.chrs.uppr.n.log 
##                            -0.0116026565 
##                D.ratio.weight.sum.wrds.n 
##                            -0.0593199934 
##               D.ratio.wrds.stop.n.wrds.n 
##                            -2.9071388332 
##                  D.terms.post.stem.n.log 
##                            -0.2864766681 
##                  D.terms.post.stop.n.log 
##                            -0.9265313808 
##                   D.weight.post.stem.sum 
##                            -0.1209650771 
##                             D.weight.sum 
##                            -0.1180951219 
##             D.weight.sum.stem.stop.Ratio 
##                            -2.9013605803 
##                             D.wrds.n.log 
##                             1.2607015439 
##                        D.wrds.stop.n.log 
##                             0.1152103538 
##                         D.wrds.unq.n.log 
##                            -0.1770678274 
##                                 biddable 
##                             2.5343114368 
##                     cellular.fctrUnknown 
##                            -0.7922522457 
##                           color.fctrGold 
##                             0.0871372337 
##                     color.fctrSpace Gray 
##                            -0.3474018561 
##                        color.fctrUnknown 
##                             0.3560862355 
##                          color.fctrWhite 
##                            -0.2253657549 
##   condition.fctrFor parts or not working 
##                            -0.7365978083 
##   condition.fctrManufacturer refurbished 
##                            -1.1969859100 
##                        condition.fctrNew 
##                             0.0297417566 
##    condition.fctrNew other (see details) 
##                             0.7229925582 
##         condition.fctrSeller refurbished 
##                            -1.0271638293 
##                        prdl.my.fctriPad1 
##                            -0.6856562063 
##                        prdl.my.fctriPad2 
##                            -0.4622200902 
##                        prdl.my.fctriPad3 
##                             0.1837795731 
##                        prdl.my.fctriPad4 
##                             1.4053112779 
##                      prdl.my.fctriPadAir 
##                             1.5486082406 
##                     prdl.my.fctriPadAir2 
##                             1.9064724435 
##                     prdl.my.fctriPadmini 
##                            -0.4755225796 
##                    prdl.my.fctriPadmini2 
##                             1.3324979842 
##                    prdl.my.fctriPadmini3 
##                             0.7573021470 
##                spdiff.cut.fctr(-100,-10] 
##                             2.3577551693 
##                  spdiff.cut.fctr(-10,-1] 
##                             4.1547129846 
##                    spdiff.cut.fctr(-1,0] 
##                             5.3348860714 
##                     spdiff.cut.fctr(0,1] 
##                             4.4072812301 
##                    spdiff.cut.fctr(1,10] 
##                             4.4073453219 
##                  spdiff.cut.fctr(10,100] 
##                             3.8533894456 
##               spdiff.cut.fctr(100,1e+03] 
##                             1.0918155713 
##                           sprice.d20nexp 
##                            -1.5657841541 
##                             sprice.log10 
##                            -0.5769819282 
##                             sprice.root2 
##                            -0.1085201562 
##                      startprice.dcm1.is9 
##                            -0.4894970169 
##                      startprice.dcm2.is9 
##                            -0.0838883551 
##                      startprice.dgt1.is9 
##                             0.0254181773 
##                      startprice.dgt2.is9 
##                             0.1157259996 
##                           storage.fctr16 
##                            -0.1300313626 
##                           storage.fctr32 
##                             0.0005589105 
##                           storage.fctr64 
##                             0.2180851783 
##                      storage.fctrUnknown 
##                             1.7336441556 
##          cellular.fctr0:carrier.fctrNone 
##                             0.1706810167 
##         cellular.fctr1:carrier.fctrOther 
##                             2.6072585524 
##        cellular.fctr1:carrier.fctrSprint 
##                             1.0270604082 
##      cellular.fctr1:carrier.fctrT-Mobile 
##                            -0.2821763037 
##       cellular.fctr1:carrier.fctrUnknown 
##                            -0.0179172973 
## cellular.fctrUnknown:carrier.fctrUnknown 
##                            -0.7883348835 
##       cellular.fctr1:carrier.fctrVerizon 
##                             0.3156224380 
##     prdl.my.fctrUnknown:.clusterid.fctr2 
##                             0.8932344300 
##       prdl.my.fctriPad1:.clusterid.fctr2 
##                            -0.7404516345 
##       prdl.my.fctriPad3:.clusterid.fctr2 
##                             0.2701330403 
##       prdl.my.fctriPad4:.clusterid.fctr2 
##                            -1.4552193885 
##     prdl.my.fctriPadAir:.clusterid.fctr2 
##                            -0.3279730199 
##   prdl.my.fctriPadmini2:.clusterid.fctr2 
##                            -0.6287767988 
##     prdl.my.fctrUnknown:.clusterid.fctr3 
##                            -0.7598398151 
##       prdl.my.fctriPad1:.clusterid.fctr3 
##                            -1.1337789530 
##       prdl.my.fctriPad4:.clusterid.fctr3 
##                            -5.9466674332 
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
##  [1] "(Intercept)"                              
##  [2] ".rnorm"                                   
##  [3] "D.chrs.n.log"                             
##  [4] "D.chrs.pnct11.n.log"                      
##  [5] "D.chrs.pnct13.n.log"                      
##  [6] "D.chrs.uppr.n.log"                        
##  [7] "D.ratio.weight.sum.wrds.n"                
##  [8] "D.ratio.wrds.stop.n.wrds.n"               
##  [9] "D.terms.post.stem.n.log"                  
## [10] "D.terms.post.stop.n.log"                  
## [11] "D.weight.post.stem.sum"                   
## [12] "D.weight.post.stop.sum"                   
## [13] "D.weight.sum"                             
## [14] "D.weight.sum.stem.stop.Ratio"             
## [15] "D.wrds.n.log"                             
## [16] "D.wrds.stop.n.log"                        
## [17] "D.wrds.unq.n.log"                         
## [18] "biddable"                                 
## [19] "cellular.fctr1"                           
## [20] "cellular.fctrUnknown"                     
## [21] "color.fctrGold"                           
## [22] "color.fctrSpace Gray"                     
## [23] "color.fctrUnknown"                        
## [24] "color.fctrWhite"                          
## [25] "condition.fctrFor parts or not working"   
## [26] "condition.fctrManufacturer refurbished"   
## [27] "condition.fctrNew"                        
## [28] "condition.fctrNew other (see details)"    
## [29] "condition.fctrSeller refurbished"         
## [30] "prdl.my.fctriPad1"                        
## [31] "prdl.my.fctriPad2"                        
## [32] "prdl.my.fctriPad3"                        
## [33] "prdl.my.fctriPad4"                        
## [34] "prdl.my.fctriPadAir"                      
## [35] "prdl.my.fctriPadAir2"                     
## [36] "prdl.my.fctriPadmini"                     
## [37] "prdl.my.fctriPadmini2"                    
## [38] "prdl.my.fctriPadmini3"                    
## [39] "spdiff.cut.fctr(-100,-10]"                
## [40] "spdiff.cut.fctr(-10,-1]"                  
## [41] "spdiff.cut.fctr(-1,0]"                    
## [42] "spdiff.cut.fctr(0,1]"                     
## [43] "spdiff.cut.fctr(1,10]"                    
## [44] "spdiff.cut.fctr(10,100]"                  
## [45] "spdiff.cut.fctr(100,1e+03]"               
## [46] "sprice.d20nexp"                           
## [47] "sprice.log10"                             
## [48] "sprice.root2"                             
## [49] "startprice.dcm1.is9"                      
## [50] "startprice.dcm2.is9"                      
## [51] "startprice.dgt1.is9"                      
## [52] "startprice.dgt2.is9"                      
## [53] "storage.fctr16"                           
## [54] "storage.fctr32"                           
## [55] "storage.fctr64"                           
## [56] "storage.fctrUnknown"                      
## [57] "cellular.fctr0:carrier.fctrNone"          
## [58] "cellular.fctr1:carrier.fctrNone"          
## [59] "cellular.fctrUnknown:carrier.fctrNone"    
## [60] "cellular.fctr0:carrier.fctrOther"         
## [61] "cellular.fctr1:carrier.fctrOther"         
## [62] "cellular.fctrUnknown:carrier.fctrOther"   
## [63] "cellular.fctr0:carrier.fctrSprint"        
## [64] "cellular.fctr1:carrier.fctrSprint"        
## [65] "cellular.fctrUnknown:carrier.fctrSprint"  
## [66] "cellular.fctr0:carrier.fctrT-Mobile"      
## [67] "cellular.fctr1:carrier.fctrT-Mobile"      
## [68] "cellular.fctrUnknown:carrier.fctrT-Mobile"
## [69] "cellular.fctr0:carrier.fctrUnknown"       
## [70] "cellular.fctr1:carrier.fctrUnknown"       
## [71] "cellular.fctrUnknown:carrier.fctrUnknown" 
## [72] "cellular.fctr0:carrier.fctrVerizon"       
## [73] "cellular.fctr1:carrier.fctrVerizon"       
## [74] "cellular.fctrUnknown:carrier.fctrVerizon" 
## [75] "prdl.my.fctrUnknown:.clusterid.fctr2"     
## [76] "prdl.my.fctriPad1:.clusterid.fctr2"       
## [77] "prdl.my.fctriPad2:.clusterid.fctr2"       
## [78] "prdl.my.fctriPad3:.clusterid.fctr2"       
## [79] "prdl.my.fctriPad4:.clusterid.fctr2"       
## [80] "prdl.my.fctriPadAir:.clusterid.fctr2"     
## [81] "prdl.my.fctriPadAir2:.clusterid.fctr2"    
## [82] "prdl.my.fctriPadmini:.clusterid.fctr2"    
## [83] "prdl.my.fctriPadmini2:.clusterid.fctr2"   
## [84] "prdl.my.fctriPadmini3:.clusterid.fctr2"   
## [85] "prdl.my.fctrUnknown:.clusterid.fctr3"     
## [86] "prdl.my.fctriPad1:.clusterid.fctr3"       
## [87] "prdl.my.fctriPad2:.clusterid.fctr3"       
## [88] "prdl.my.fctriPad3:.clusterid.fctr3"       
## [89] "prdl.my.fctriPad4:.clusterid.fctr3"       
## [90] "prdl.my.fctriPadAir:.clusterid.fctr3"     
## [91] "prdl.my.fctriPadAir2:.clusterid.fctr3"    
## [92] "prdl.my.fctriPadmini:.clusterid.fctr3"    
## [93] "prdl.my.fctriPadmini2:.clusterid.fctr3"   
## [94] "prdl.my.fctriPadmini3:.clusterid.fctr3"

##   sold.fctr sold.fctr.predict.All.X.glmnet.N
## 1         N                              467
## 2         Y                               63
##   sold.fctr.predict.All.X.glmnet.Y
## 1                               81
## 2                              409
##          Prediction
## Reference   N   Y
##         N 467  81
##         Y  63 409
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   8.588235e-01   7.168196e-01   8.359295e-01   8.796230e-01   5.372549e-01 
## AccuracyPValue  McnemarPValue 
##  2.677951e-106   1.565804e-01

##   sold.fctr sold.fctr.predict.All.X.glmnet.N
## 1         N                              309
## 2         Y                               60
##   sold.fctr.predict.All.X.glmnet.Y
## 1                              138
## 2                              323
##          Prediction
## Reference   N   Y
##         N 309 138
##         Y  60 323
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.614458e-01   5.269348e-01   7.309494e-01   7.900724e-01   5.385542e-01 
## AccuracyPValue  McnemarPValue 
##   2.413828e-40   4.446039e-08 
##             id
## 1 All.X.glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    feats
## 1 biddable,sprice.d20nexp,spdiff.cut.fctr,D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,startprice.dcm1.is9,storage.fctr,D.chrs.pnct11.n.log,D.chrs.pnct13.n.log,startprice.dgt2.is9,color.fctr,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,prdl.my.fctr,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.log10,sprice.root2,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     14.826                 0.545
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.8626593    0.9032847    0.8220339       0.9355785
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.4       0.8503119        0.8303886
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.8359295              0.879623     0.6576319
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.7835089    0.8255034    0.7415144       0.8721444
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.3       0.7654028        0.7614458
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.7309494             0.7900724     0.5269348
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01126649      0.02276618
##                        label step_major step_minor label_minor     bgn
## 6         fit.models_1_All.X          3          1      glmnet 451.384
## 7 fit.models_1_Best.Interact          4          0       setup 470.799
##       end elapsed
## 6 470.799  19.415
## 7      NA      NA
##                                     label step_major step_minor
## 7              fit.models_1_Best.Interact          4          0
## 8 fit.models_1_Max.cor.Y.rcv.1X1.Interact          4          1
##   label_minor     bgn    end elapsed
## 7       setup 470.799 470.84   0.041
## 8      glmnet 470.840     NA      NA
## [1] "fitting model: Max.cor.Y.rcv.1X1.Interact.glmnet"
## [1] "    indep_vars: sprice.root2,biddable"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.124 on full training set
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0           67    -none-     numeric  
## beta        134    dgCMatrix  S4       
## df           67    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       67    -none-     numeric  
## dev.ratio    67    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        2    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##  (Intercept)     biddable sprice.root2 
##   0.36344652   1.18295779  -0.08216503 
## [1] "max lambda < lambdaOpt:"
##  (Intercept)     biddable sprice.root2 
##   0.39118688   1.22752496  -0.08592317

##   sold.fctr sold.fctr.predict.Max.cor.Y.rcv.1X1.Interact.glmnet.N
## 1         N                                                   457
## 2         Y                                                   118
##   sold.fctr.predict.Max.cor.Y.rcv.1X1.Interact.glmnet.Y
## 1                                                    91
## 2                                                   354
##          Prediction
## Reference   N   Y
##         N 457  91
##         Y 118 354
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.950980e-01   5.862671e-01   7.690020e-01   8.194783e-01   5.372549e-01 
## AccuracyPValue  McnemarPValue 
##   3.331019e-66   7.210452e-02

##   sold.fctr sold.fctr.predict.Max.cor.Y.rcv.1X1.Interact.glmnet.N
## 1         N                                                   341
## 2         Y                                                    88
##   sold.fctr.predict.Max.cor.Y.rcv.1X1.Interact.glmnet.Y
## 1                                                   106
## 2                                                   295
##          Prediction
## Reference   N   Y
##         N 341 106
##         Y  88 295
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.662651e-01   5.313109e-01   7.359543e-01   7.946712e-01   5.385542e-01 
## AccuracyPValue  McnemarPValue 
##   4.058545e-42   2.222645e-01 
##                                  id                 feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1.Interact.glmnet sprice.root2,biddable              25
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      2.319                 0.016       0.7919708
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.8339416         0.75       0.8622147                    0.5
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.7720829        0.7954221              0.769002
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.8194783     0.5866808        0.760384    0.8210291
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.6997389        0.819747                    0.4        0.752551
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.7662651             0.7359543             0.7946712
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1     0.5313109          0.0200887      0.04087803
# Check if other preProcess methods improve model performance
mdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indep_vars_vctr <- 
    trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse=".")
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
    fitobs_df <- glb_fitobs_df[!(glb_fitobs_df[, glb_id_var] %in%
                                     glbObsFitOutliers[[mdl_id_pfx]]), ]
} else fitobs_df <- glb_fitobs_df

for (prePr in glb_preproc_methods) {   
    # The operations are applied in this order: 
    #   Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
    
    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
            id.prefix=mdl_id_pfx, 
            type=glb_model_type, tune.df=glb_tune_models_df,
            trainControl.method="repeatedcv",
            trainControl.number=glb_rcv_n_folds,
            trainControl.repeats=glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method=method, train.preProcess=prePr)),
            indep_vars=indep_vars_vctr, rsp_var=glb_rsp_var, 
            fit_df=fitobs_df, OOB_df=glb_OOBobs_df)
}            

    # If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
    #   check NA coefficients & filter appropriate terms in indep_vars_vctr
#     if (method == "glm") {
#         orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
#         orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
#         orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
#           require(car)
#           vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
#           # if vif errors out with "there are aliased coefficients in the model"
#               alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
#           print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
#           print(which.max(vif_orig_glm))
#           print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
#           glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
#           glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in%    grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
#           all.equal(glb_allobs_df$S.chrs.uppr.n.log, glb_allobs_df$A.chrs.uppr.n.log)
#           cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
#           mydsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
#           subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
#         corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
#           which.max(abs_corxx_mtrx["S.T.tribun", ])
#           abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
#         step_glm <- step(orig_glm)
#     }
    # Since caret does not optimize rpart well
#     if (method == "rpart")
#         ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
#                                 indep_vars_vctr=indep_vars_vctr,
#                                 model_type=glb_model_type,
#                                 rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
#                                 fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,        
#             n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))

# User specified
#   Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df

    # easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
#                         , 1)[, "feats"]
# indep_vars_vctr <- trim(unlist(strsplit(indep_vars_vctr, "[,]")))
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")

    # easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indep_vars_vctr <- c(NULL
#     ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
#     ,"prdline.my.fctr*biddable"
#     #,"prdline.my.fctr*startprice.log"
#     #,"prdline.my.fctr*startprice.diff"    
#     ,"prdline.my.fctr*condition.fctr"
#     ,"prdline.my.fctr*D.terms.post.stop.n"
#     #,"prdline.my.fctr*D.terms.post.stem.n"
#     ,"prdline.my.fctr*cellular.fctr"    
# #    ,"<feat1>:<feat2>"
#                                            )
# for (method in glbMdlMethods) {
#     ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
#                                 indep_vars_vctr=indep_vars_vctr,
#                                 model_type=glb_model_type,
#                                 rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
#                                 fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
#                     n_cv_folds=glb_rcv_n_folds, tune_models_df=glb_tune_models_df)
#     csm_mdl_id <- paste0(mdl_id, ".", method)
#     csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
#                                                                      method)]]);               print(head(csm_featsimp_df))
# }
###

# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glb_trnobs_df); print(summary(Ntv.1.lm))

#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$importance)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$importance)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]

    # User specified bivariate models
#     indep_vars_vctr_lst <- list()
#     for (feat in setdiff(names(glb_fitobs_df), 
#                          union(glb_rsp_var, glbFeatsExclude)))
#         indep_vars_vctr_lst[["feat"]] <- feat

    # User specified combinatorial models
#     indep_vars_vctr_lst <- list()
#     combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"), 
#                           <num_feats_to_choose>)
#     for (combn_ix in 1:ncol(combn_mtrx))
#         #print(combn_mtrx[, combn_ix])
#         indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
    
    # template for myfit_mdl
    #   rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
    #       only for OOB in trainControl ?
    
#     ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
#                             indep_vars_vctr=indep_vars_vctr,
#                             rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
#                             fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
#                             n_cv_folds=glb_rcv_n_folds, tune_models_df=glb_tune_models_df,
#                             model_loss_mtrx=glbMdlMetric_terms,
#                             model_summaryFunction=glbMdlMetricSummaryFn,
#                             model_metric=glbMdlMetricSummary,
#                             model_metric_maximize=glbMdlMetricMaximize)

# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)

# Non-caret models
#     rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var), 
#                                data=glb_fitobs_df, #method="class", 
#                                control=rpart.control(cp=0.12),
#                            parms=list(loss=glbMdlMetric_terms))
#     print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
# 

print(glb_models_df)
##                                                                  id
## MFO.myMFO_classfr                                 MFO.myMFO_classfr
## Random.myrandom_classfr                     Random.myrandom_classfr
## Max.cor.Y.rcv.1X1.glmnet                   Max.cor.Y.rcv.1X1.glmnet
## Max.cor.Y.rcv.3X1.glmnet                   Max.cor.Y.rcv.3X1.glmnet
## Max.cor.Y.rcv.3X3.glmnet                   Max.cor.Y.rcv.3X3.glmnet
## Max.cor.Y.rcv.3X5.glmnet                   Max.cor.Y.rcv.3X5.glmnet
## Max.cor.Y.rcv.5X1.glmnet                   Max.cor.Y.rcv.5X1.glmnet
## Max.cor.Y.rcv.5X3.glmnet                   Max.cor.Y.rcv.5X3.glmnet
## Max.cor.Y.rcv.5X5.glmnet                   Max.cor.Y.rcv.5X5.glmnet
## Max.cor.Y.rcv.1X1.cp.0.rpart           Max.cor.Y.rcv.1X1.cp.0.rpart
## Max.cor.Y.rpart                                     Max.cor.Y.rpart
## Interact.High.cor.Y.glmnet               Interact.High.cor.Y.glmnet
## Low.cor.X.glmnet                                   Low.cor.X.glmnet
## RFE.X.glmnet                                           RFE.X.glmnet
## RFE.X.glm                                                 RFE.X.glm
## All.X.glmnet                                           All.X.glmnet
## Max.cor.Y.rcv.1X1.Interact.glmnet Max.cor.Y.rcv.1X1.Interact.glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    feats
## MFO.myMFO_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 .rnorm
## Random.myrandom_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           .rnorm
## Max.cor.Y.rcv.1X1.glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           biddable,sprice.root2
## Max.cor.Y.rcv.3X1.glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           biddable,sprice.root2
## Max.cor.Y.rcv.3X3.glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           biddable,sprice.root2
## Max.cor.Y.rcv.3X5.glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           biddable,sprice.root2
## Max.cor.Y.rcv.5X1.glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           biddable,sprice.root2
## Max.cor.Y.rcv.5X3.glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           biddable,sprice.root2
## Max.cor.Y.rcv.5X5.glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           biddable,sprice.root2
## Max.cor.Y.rcv.1X1.cp.0.rpart                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       biddable,sprice.root2
## Max.cor.Y.rpart                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    biddable,sprice.root2
## Interact.High.cor.Y.glmnet                                                                                                                                                                                                                                                                                                                                                                 biddable,sprice.root2,biddable:sprice.log10,biddable:D.terms.post.stop.n.log,biddable:startprice.dcm2.is9,biddable:D.weight.post.stem.sum,biddable:D.chrs.n.log,biddable:cellular.fctr,biddable:D.terms.post.stem.n.log,biddable:sprice.root2
## Low.cor.X.glmnet                                                                                                                                                                                                                                                                                            biddable,spdiff.cut.fctr,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,storage.fctr,D.chrs.pnct11.n.log,startprice.dgt2.is9,color.fctr,prdl.my.fctr,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.root2,prdl.my.fctr:.clusterid.fctr
## RFE.X.glmnet                      sprice.d20nexp,sprice.log10,sprice.root2,biddable,spdiff.cut.fctr,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,D.terms.post.stem.n.log,D.chrs.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## RFE.X.glm                         sprice.d20nexp,sprice.log10,sprice.root2,biddable,spdiff.cut.fctr,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,D.terms.post.stem.n.log,D.chrs.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## All.X.glmnet                      biddable,sprice.d20nexp,spdiff.cut.fctr,D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,startprice.dcm1.is9,storage.fctr,D.chrs.pnct11.n.log,D.chrs.pnct13.n.log,startprice.dgt2.is9,color.fctr,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,prdl.my.fctr,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.log10,sprice.root2,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## Max.cor.Y.rcv.1X1.Interact.glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  sprice.root2,biddable
##                                   max.nTuningRuns
## MFO.myMFO_classfr                               0
## Random.myrandom_classfr                         0
## Max.cor.Y.rcv.1X1.glmnet                        0
## Max.cor.Y.rcv.3X1.glmnet                       25
## Max.cor.Y.rcv.3X3.glmnet                       25
## Max.cor.Y.rcv.3X5.glmnet                       25
## Max.cor.Y.rcv.5X1.glmnet                       25
## Max.cor.Y.rcv.5X3.glmnet                       25
## Max.cor.Y.rcv.5X5.glmnet                       25
## Max.cor.Y.rcv.1X1.cp.0.rpart                    0
## Max.cor.Y.rpart                                 5
## Interact.High.cor.Y.glmnet                     25
## Low.cor.X.glmnet                               25
## RFE.X.glmnet                                   25
## RFE.X.glm                                       1
## All.X.glmnet                                   25
## Max.cor.Y.rcv.1X1.Interact.glmnet              25
##                                   min.elapsedtime.everything
## MFO.myMFO_classfr                                      0.292
## Random.myrandom_classfr                                0.308
## Max.cor.Y.rcv.1X1.glmnet                               0.740
## Max.cor.Y.rcv.3X1.glmnet                               1.546
## Max.cor.Y.rcv.3X3.glmnet                               2.078
## Max.cor.Y.rcv.3X5.glmnet                               2.568
## Max.cor.Y.rcv.5X1.glmnet                               1.729
## Max.cor.Y.rcv.5X3.glmnet                               2.597
## Max.cor.Y.rcv.5X5.glmnet                               3.111
## Max.cor.Y.rcv.1X1.cp.0.rpart                           0.750
## Max.cor.Y.rpart                                        1.632
## Interact.High.cor.Y.glmnet                             2.747
## Low.cor.X.glmnet                                       3.129
## RFE.X.glmnet                                          15.219
## RFE.X.glm                                              2.890
## All.X.glmnet                                          14.826
## Max.cor.Y.rcv.1X1.Interact.glmnet                      2.319
##                                   min.elapsedtime.final max.AUCpROC.fit
## MFO.myMFO_classfr                                 0.003       0.5000000
## Random.myrandom_classfr                           0.001       0.4859659
## Max.cor.Y.rcv.1X1.glmnet                          0.023       0.7898522
## Max.cor.Y.rcv.3X1.glmnet                          0.018       0.7919708
## Max.cor.Y.rcv.3X3.glmnet                          0.016       0.7919708
## Max.cor.Y.rcv.3X5.glmnet                          0.016       0.7919708
## Max.cor.Y.rcv.5X1.glmnet                          0.015       0.7956204
## Max.cor.Y.rcv.5X3.glmnet                          0.016       0.7956204
## Max.cor.Y.rcv.5X5.glmnet                          0.015       0.7963859
## Max.cor.Y.rcv.1X1.cp.0.rpart                      0.012       0.8281424
## Max.cor.Y.rpart                                   0.013       0.8102808
## Interact.High.cor.Y.glmnet                        0.100       0.7970973
## Low.cor.X.glmnet                                  0.121       0.8597751
## RFE.X.glmnet                                      0.534       0.8626593
## RFE.X.glm                                         0.192       0.8597751
## All.X.glmnet                                      0.545       0.8626593
## Max.cor.Y.rcv.1X1.Interact.glmnet                 0.016       0.7919708
##                                   max.Sens.fit max.Spec.fit
## MFO.myMFO_classfr                    1.0000000    0.0000000
## Random.myrandom_classfr              0.5164234    0.4555085
## Max.cor.Y.rcv.1X1.glmnet             0.8339416    0.7457627
## Max.cor.Y.rcv.3X1.glmnet             0.8339416    0.7500000
## Max.cor.Y.rcv.3X3.glmnet             0.8339416    0.7500000
## Max.cor.Y.rcv.3X5.glmnet             0.8339416    0.7500000
## Max.cor.Y.rcv.5X1.glmnet             0.8412409    0.7500000
## Max.cor.Y.rcv.5X3.glmnet             0.8412409    0.7500000
## Max.cor.Y.rcv.5X5.glmnet             0.8448905    0.7478814
## Max.cor.Y.rcv.1X1.cp.0.rpart         0.8978102    0.7584746
## Max.cor.Y.rpart                      0.8959854    0.7245763
## Interact.High.cor.Y.glmnet           0.8886861    0.7055085
## Low.cor.X.glmnet                     0.8996350    0.8199153
## RFE.X.glmnet                         0.9032847    0.8220339
## RFE.X.glm                            0.8996350    0.8199153
## All.X.glmnet                         0.9032847    0.8220339
## Max.cor.Y.rcv.1X1.Interact.glmnet    0.8339416    0.7500000
##                                   max.AUCROCR.fit opt.prob.threshold.fit
## MFO.myMFO_classfr                       0.5000000                    0.4
## Random.myrandom_classfr                 0.4893217                    0.4
## Max.cor.Y.rcv.1X1.glmnet                0.8620600                    0.4
## Max.cor.Y.rcv.3X1.glmnet                0.8621335                    0.5
## Max.cor.Y.rcv.3X3.glmnet                0.8622147                    0.5
## Max.cor.Y.rcv.3X5.glmnet                0.8622069                    0.5
## Max.cor.Y.rcv.5X1.glmnet                0.8621064                    0.5
## Max.cor.Y.rcv.5X3.glmnet                0.8621064                    0.5
## Max.cor.Y.rcv.5X5.glmnet                0.8621064                    0.5
## Max.cor.Y.rcv.1X1.cp.0.rpart            0.8816304                    0.4
## Max.cor.Y.rpart                         0.8387008                    0.5
## Interact.High.cor.Y.glmnet              0.8639370                    0.4
## Low.cor.X.glmnet                        0.9329264                    0.5
## RFE.X.glmnet                            0.9355785                    0.4
## RFE.X.glm                               0.9392823                    0.4
## All.X.glmnet                            0.9355785                    0.4
## Max.cor.Y.rcv.1X1.Interact.glmnet       0.8622147                    0.5
##                                   max.f.score.fit max.Accuracy.fit
## MFO.myMFO_classfr                       0.6327078        0.4627451
## Random.myrandom_classfr                 0.6327078        0.4627451
## Max.cor.Y.rcv.1X1.glmnet                0.7698745        0.7843137
## Max.cor.Y.rcv.3X1.glmnet                0.7720829        0.7950911
## Max.cor.Y.rcv.3X3.glmnet                0.7720829        0.7954221
## Max.cor.Y.rcv.3X5.glmnet                0.7720829        0.7962655
## Max.cor.Y.rcv.5X1.glmnet                0.7754655        0.7990899
## Max.cor.Y.rcv.5X3.glmnet                0.7754655        0.7964364
## Max.cor.Y.rcv.5X5.glmnet                0.7758242        0.7970687
## Max.cor.Y.rcv.1X1.cp.0.rpart            0.8102109        0.8323529
## Max.cor.Y.rpart                         0.7853042        0.7964073
## Interact.High.cor.Y.glmnet              0.7724750        0.8026098
## Low.cor.X.glmnet                        0.8468271        0.8349657
## RFE.X.glmnet                            0.8503119        0.8303886
## RFE.X.glm                               0.8496868        0.8307135
## All.X.glmnet                            0.8503119        0.8303886
## Max.cor.Y.rcv.1X1.Interact.glmnet       0.7720829        0.7954221
##                                   max.AccuracyLower.fit
## MFO.myMFO_classfr                             0.4318011
## Random.myrandom_classfr                       0.4318011
## Max.cor.Y.rcv.1X1.glmnet                      0.7577802
## Max.cor.Y.rcv.3X1.glmnet                      0.7690020
## Max.cor.Y.rcv.3X3.glmnet                      0.7690020
## Max.cor.Y.rcv.3X5.glmnet                      0.7690020
## Max.cor.Y.rcv.5X1.glmnet                      0.7730893
## Max.cor.Y.rcv.5X3.glmnet                      0.7730893
## Max.cor.Y.rcv.5X5.glmnet                      0.7741117
## Max.cor.Y.rcv.1X1.cp.0.rpart                  0.8079872
## Max.cor.Y.rpart                               0.7915283
## Interact.High.cor.Y.glmnet                    0.7730893
## Low.cor.X.glmnet                              0.8400893
## RFE.X.glmnet                                  0.8359295
## RFE.X.glm                                     0.8359295
## All.X.glmnet                                  0.8359295
## Max.cor.Y.rcv.1X1.Interact.glmnet             0.7690020
##                                   max.AccuracyUpper.fit max.Kappa.fit
## MFO.myMFO_classfr                             0.4939047     0.0000000
## Random.myrandom_classfr                       0.4939047     0.0000000
## Max.cor.Y.rcv.1X1.glmnet                      0.8091950     0.5669826
## Max.cor.Y.rcv.3X1.glmnet                      0.8194783     0.5860164
## Max.cor.Y.rcv.3X3.glmnet                      0.8194783     0.5866808
## Max.cor.Y.rcv.3X5.glmnet                      0.8194783     0.5883400
## Max.cor.Y.rcv.5X1.glmnet                      0.8232111     0.5944245
## Max.cor.Y.rcv.5X3.glmnet                      0.8232111     0.5888026
## Max.cor.Y.rcv.5X5.glmnet                      0.8241437     0.5901123
## Max.cor.Y.rcv.1X1.cp.0.rpart                  0.8547824     0.6606905
## Max.cor.Y.rpart                               0.8399619     0.5852116
## Interact.High.cor.Y.glmnet                    0.8232111     0.5988972
## Low.cor.X.glmnet                              0.8832827     0.6666179
## RFE.X.glmnet                                  0.8796230     0.6576319
## RFE.X.glm                                     0.8796230     0.6585198
## All.X.glmnet                                  0.8796230     0.6576319
## Max.cor.Y.rcv.1X1.Interact.glmnet             0.8194783     0.5866808
##                                   max.AUCpROC.OOB max.Sens.OOB
## MFO.myMFO_classfr                       0.5000000    1.0000000
## Random.myrandom_classfr                 0.5064252    0.5480984
## Max.cor.Y.rcv.1X1.glmnet                0.7665376    0.8255034
## Max.cor.Y.rcv.3X1.glmnet                0.7592654    0.8187919
## Max.cor.Y.rcv.3X3.glmnet                0.7603840    0.8210291
## Max.cor.Y.rcv.3X5.glmnet                0.7603840    0.8210291
## Max.cor.Y.rcv.5X1.glmnet                0.7615026    0.8232662
## Max.cor.Y.rcv.5X3.glmnet                0.7615026    0.8232662
## Max.cor.Y.rcv.5X5.glmnet                0.7641135    0.8232662
## Max.cor.Y.rcv.1X1.cp.0.rpart            0.7504950    0.8456376
## Max.cor.Y.rpart                         0.7445254    0.8545861
## Interact.High.cor.Y.glmnet              0.7551533    0.8680089
## Low.cor.X.glmnet                        0.7801532    0.8187919
## RFE.X.glmnet                            0.7835089    0.8255034
## RFE.X.glm                               0.7851882    0.8210291
## All.X.glmnet                            0.7835089    0.8255034
## Max.cor.Y.rcv.1X1.Interact.glmnet       0.7603840    0.8210291
##                                   max.Spec.OOB max.AUCROCR.OOB
## MFO.myMFO_classfr                    0.0000000       0.5000000
## Random.myrandom_classfr              0.4647520       0.4956046
## Max.cor.Y.rcv.1X1.glmnet             0.7075718       0.8197236
## Max.cor.Y.rcv.3X1.glmnet             0.6997389       0.8197353
## Max.cor.Y.rcv.3X3.glmnet             0.6997389       0.8197470
## Max.cor.Y.rcv.3X5.glmnet             0.6997389       0.8196593
## Max.cor.Y.rcv.5X1.glmnet             0.6997389       0.8195600
## Max.cor.Y.rcv.5X3.glmnet             0.6997389       0.8195600
## Max.cor.Y.rcv.5X5.glmnet             0.7049608       0.8193790
## Max.cor.Y.rcv.1X1.cp.0.rpart         0.6553525       0.7934621
## Max.cor.Y.rpart                      0.6344648       0.7855854
## Interact.High.cor.Y.glmnet           0.6422977       0.8189000
## Low.cor.X.glmnet                     0.7415144       0.8727052
## RFE.X.glmnet                         0.7415144       0.8721444
## RFE.X.glm                            0.7493473       0.8700475
## All.X.glmnet                         0.7415144       0.8721444
## Max.cor.Y.rcv.1X1.Interact.glmnet    0.6997389       0.8197470
##                                   opt.prob.threshold.OOB max.f.score.OOB
## MFO.myMFO_classfr                                    0.4       0.6314922
## Random.myrandom_classfr                              0.4       0.6314922
## Max.cor.Y.rcv.1X1.glmnet                             0.4       0.7535854
## Max.cor.Y.rcv.3X1.glmnet                             0.4       0.7519182
## Max.cor.Y.rcv.3X3.glmnet                             0.4       0.7525510
## Max.cor.Y.rcv.3X5.glmnet                             0.4       0.7515924
## Max.cor.Y.rcv.5X1.glmnet                             0.4       0.7506361
## Max.cor.Y.rcv.5X3.glmnet                             0.4       0.7506361
## Max.cor.Y.rcv.5X5.glmnet                             0.4       0.7512690
## Max.cor.Y.rcv.1X1.cp.0.rpart                         0.3       0.7313997
## Max.cor.Y.rpart                                      0.3       0.7394270
## Interact.High.cor.Y.glmnet                           0.3       0.7522698
## Low.cor.X.glmnet                                     0.3       0.7719715
## RFE.X.glmnet                                         0.3       0.7654028
## RFE.X.glm                                            0.5       0.7653333
## All.X.glmnet                                         0.3       0.7654028
## Max.cor.Y.rcv.1X1.Interact.glmnet                    0.4       0.7525510
##                                   max.Accuracy.OOB max.AccuracyLower.OOB
## MFO.myMFO_classfr                        0.4614458             0.4271177
## Random.myrandom_classfr                  0.4614458             0.4271177
## Max.cor.Y.rcv.1X1.glmnet                 0.7722892             0.7422177
## Max.cor.Y.rcv.3X1.glmnet                 0.7662651             0.7359543
## Max.cor.Y.rcv.3X3.glmnet                 0.7662651             0.7359543
## Max.cor.Y.rcv.3X5.glmnet                 0.7650602             0.7347026
## Max.cor.Y.rcv.5X1.glmnet                 0.7638554             0.7334512
## Max.cor.Y.rcv.5X3.glmnet                 0.7638554             0.7334512
## Max.cor.Y.rcv.5X5.glmnet                 0.7638554             0.7334512
## Max.cor.Y.rcv.1X1.cp.0.rpart             0.7433735             0.7122254
## Max.cor.Y.rpart                          0.7698795             0.7397113
## Interact.High.cor.Y.glmnet               0.7698795             0.7397113
## Low.cor.X.glmnet                         0.7686747             0.7384586
## RFE.X.glmnet                             0.7614458             0.7309494
## RFE.X.glm                                0.7879518             0.7585420
## All.X.glmnet                             0.7614458             0.7309494
## Max.cor.Y.rcv.1X1.Interact.glmnet        0.7662651             0.7359543
##                                   max.AccuracyUpper.OOB max.Kappa.OOB
## MFO.myMFO_classfr                             0.4960486     0.0000000
## Random.myrandom_classfr                       0.4960486     0.0000000
## Max.cor.Y.rcv.1X1.glmnet                      0.8004125     0.5419399
## Max.cor.Y.rcv.3X1.glmnet                      0.7946712     0.5311363
## Max.cor.Y.rcv.3X3.glmnet                      0.7946712     0.5313109
## Max.cor.Y.rcv.3X5.glmnet                      0.7935220     0.5289828
## Max.cor.Y.rcv.5X1.glmnet                      0.7923725     0.5266555
## Max.cor.Y.rcv.5X3.glmnet                      0.7923725     0.5266555
## Max.cor.Y.rcv.5X5.glmnet                      0.7923725     0.5268317
## Max.cor.Y.rcv.1X1.cp.0.rpart                  0.7727821     0.4862697
## Max.cor.Y.rpart                               0.7981170     0.5341327
## Interact.High.cor.Y.glmnet                    0.7981170     0.5374385
## Low.cor.X.glmnet                              0.7969687     0.5411011
## RFE.X.glmnet                                  0.7900724     0.5269348
## RFE.X.glm                                     0.8152999     0.5720872
## All.X.glmnet                                  0.7900724     0.5269348
## Max.cor.Y.rcv.1X1.Interact.glmnet             0.7946712     0.5313109
##                                   max.AccuracySD.fit max.KappaSD.fit
## MFO.myMFO_classfr                                 NA              NA
## Random.myrandom_classfr                           NA              NA
## Max.cor.Y.rcv.1X1.glmnet                          NA              NA
## Max.cor.Y.rcv.3X1.glmnet                 0.003934893     0.009324777
## Max.cor.Y.rcv.3X3.glmnet                 0.020088705     0.040878031
## Max.cor.Y.rcv.3X5.glmnet                 0.018120665     0.036897112
## Max.cor.Y.rcv.5X1.glmnet                 0.036086436     0.070687264
## Max.cor.Y.rcv.5X3.glmnet                 0.033491865     0.067292776
## Max.cor.Y.rcv.5X5.glmnet                 0.031290870     0.062857472
## Max.cor.Y.rcv.1X1.cp.0.rpart                      NA              NA
## Max.cor.Y.rpart                          0.020068991     0.040059154
## Interact.High.cor.Y.glmnet               0.017969767     0.037056533
## Low.cor.X.glmnet                         0.009378705     0.019384018
## RFE.X.glmnet                             0.011266493     0.022766184
## RFE.X.glm                                0.014477821     0.029001944
## All.X.glmnet                             0.011266493     0.022766184
## Max.cor.Y.rcv.1X1.Interact.glmnet        0.020088705     0.040878031
rm(ret_lst)
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end", 
                                     major.inc=TRUE, label.minor="teardown")
##                                     label step_major step_minor
## 8 fit.models_1_Max.cor.Y.rcv.1X1.Interact          4          1
## 9                        fit.models_1_end          5          0
##   label_minor     bgn     end elapsed
## 8      glmnet 470.840 477.204   6.365
## 9    teardown 477.205      NA      NA
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)
##         label step_major step_minor label_minor     bgn     end elapsed
## 11 fit.models          6          1           1 409.619 477.214  67.595
## 12 fit.models          6          2           2 477.215      NA      NA
#stop(here"); glb_to_sav(); all.equal(glb_models_df, sav_models_df)
# if (!is.null(glbMdlMetricSummaryFn)) {
#     stats_df <- glb_models_df[, "id", FALSE]
# 
#     stats_mdl_df <- data.frame()
#     for (mdl_id in stats_df$id) {
#         stats_mdl_df <- rbind(stats_mdl_df, 
#             mypredict_mdl(glb_models_lst[[mdl_id]], glb_fitobs_df, glb_rsp_var, 
#                           glb_rsp_var_out, mdl_id, "fit",
#                               glbMdlMetricSummaryFn, glbMdlMetricSummary, 
#                               glbMdlMetricMaximize, ret_type="stats"))
#     }
#     stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
#     
#     stats_mdl_df <- data.frame()
#     for (mdl_id in stats_df$id) {
#         stats_mdl_df <- rbind(stats_mdl_df, 
#             mypredict_mdl(glb_models_lst[[mdl_id]], glb_OOBobs_df, glb_rsp_var, 
#                           glb_rsp_var_out, mdl_id, "OOB",
#                               glbMdlMetricSummaryFn, glbMdlMetricSummary, 
#                               glbMdlMetricMaximize, ret_type="stats"))
#     }
#     stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
#     
#     print("Merging following data into glb_models_df:")
#     print(stats_mrg_df <- stats_df[, c(1, grep(glbMdlMetricSummary, names(stats_df)))])
#     print(tmp_models_df <- orderBy(~id, glb_models_df[, c("id",
#                                     grep(glbMdlMetricSummary, names(stats_df), value=TRUE))]))
# 
#     tmp2_models_df <- glb_models_df[, c("id", setdiff(names(glb_models_df),
#                                     grep(glbMdlMetricSummary, names(stats_df), value=TRUE)))]
#     tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
#     print(tmp3_models_df)
#     print(names(tmp3_models_df))
#     print(glb_models_df <- subset(tmp3_models_df, select=-id.1))
# }

plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
    plt_models_df[, sub("min.", "inv.", var)] <- 
        #ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
        1.0 / plt_models_df[, var]
    plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)
##                                                                  id
## MFO.myMFO_classfr                                 MFO.myMFO_classfr
## Random.myrandom_classfr                     Random.myrandom_classfr
## Max.cor.Y.rcv.1X1.glmnet                   Max.cor.Y.rcv.1X1.glmnet
## Max.cor.Y.rcv.3X1.glmnet                   Max.cor.Y.rcv.3X1.glmnet
## Max.cor.Y.rcv.3X3.glmnet                   Max.cor.Y.rcv.3X3.glmnet
## Max.cor.Y.rcv.3X5.glmnet                   Max.cor.Y.rcv.3X5.glmnet
## Max.cor.Y.rcv.5X1.glmnet                   Max.cor.Y.rcv.5X1.glmnet
## Max.cor.Y.rcv.5X3.glmnet                   Max.cor.Y.rcv.5X3.glmnet
## Max.cor.Y.rcv.5X5.glmnet                   Max.cor.Y.rcv.5X5.glmnet
## Max.cor.Y.rcv.1X1.cp.0.rpart           Max.cor.Y.rcv.1X1.cp.0.rpart
## Max.cor.Y.rpart                                     Max.cor.Y.rpart
## Interact.High.cor.Y.glmnet               Interact.High.cor.Y.glmnet
## Low.cor.X.glmnet                                   Low.cor.X.glmnet
## RFE.X.glmnet                                           RFE.X.glmnet
## RFE.X.glm                                                 RFE.X.glm
## All.X.glmnet                                           All.X.glmnet
## Max.cor.Y.rcv.1X1.Interact.glmnet Max.cor.Y.rcv.1X1.Interact.glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    feats
## MFO.myMFO_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 .rnorm
## Random.myrandom_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           .rnorm
## Max.cor.Y.rcv.1X1.glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           biddable,sprice.root2
## Max.cor.Y.rcv.3X1.glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           biddable,sprice.root2
## Max.cor.Y.rcv.3X3.glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           biddable,sprice.root2
## Max.cor.Y.rcv.3X5.glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           biddable,sprice.root2
## Max.cor.Y.rcv.5X1.glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           biddable,sprice.root2
## Max.cor.Y.rcv.5X3.glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           biddable,sprice.root2
## Max.cor.Y.rcv.5X5.glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           biddable,sprice.root2
## Max.cor.Y.rcv.1X1.cp.0.rpart                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       biddable,sprice.root2
## Max.cor.Y.rpart                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    biddable,sprice.root2
## Interact.High.cor.Y.glmnet                                                                                                                                                                                                                                                                                                                                                                 biddable,sprice.root2,biddable:sprice.log10,biddable:D.terms.post.stop.n.log,biddable:startprice.dcm2.is9,biddable:D.weight.post.stem.sum,biddable:D.chrs.n.log,biddable:cellular.fctr,biddable:D.terms.post.stem.n.log,biddable:sprice.root2
## Low.cor.X.glmnet                                                                                                                                                                                                                                                                                            biddable,spdiff.cut.fctr,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,storage.fctr,D.chrs.pnct11.n.log,startprice.dgt2.is9,color.fctr,prdl.my.fctr,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.root2,prdl.my.fctr:.clusterid.fctr
## RFE.X.glmnet                      sprice.d20nexp,sprice.log10,sprice.root2,biddable,spdiff.cut.fctr,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,D.terms.post.stem.n.log,D.chrs.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## RFE.X.glm                         sprice.d20nexp,sprice.log10,sprice.root2,biddable,spdiff.cut.fctr,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,D.terms.post.stem.n.log,D.chrs.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## All.X.glmnet                      biddable,sprice.d20nexp,spdiff.cut.fctr,D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,startprice.dcm1.is9,storage.fctr,D.chrs.pnct11.n.log,D.chrs.pnct13.n.log,startprice.dgt2.is9,color.fctr,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,prdl.my.fctr,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.log10,sprice.root2,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## Max.cor.Y.rcv.1X1.Interact.glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  sprice.root2,biddable
##                                   max.nTuningRuns max.AUCpROC.fit
## MFO.myMFO_classfr                               0       0.5000000
## Random.myrandom_classfr                         0       0.4859659
## Max.cor.Y.rcv.1X1.glmnet                        0       0.7898522
## Max.cor.Y.rcv.3X1.glmnet                       25       0.7919708
## Max.cor.Y.rcv.3X3.glmnet                       25       0.7919708
## Max.cor.Y.rcv.3X5.glmnet                       25       0.7919708
## Max.cor.Y.rcv.5X1.glmnet                       25       0.7956204
## Max.cor.Y.rcv.5X3.glmnet                       25       0.7956204
## Max.cor.Y.rcv.5X5.glmnet                       25       0.7963859
## Max.cor.Y.rcv.1X1.cp.0.rpart                    0       0.8281424
## Max.cor.Y.rpart                                 5       0.8102808
## Interact.High.cor.Y.glmnet                     25       0.7970973
## Low.cor.X.glmnet                               25       0.8597751
## RFE.X.glmnet                                   25       0.8626593
## RFE.X.glm                                       1       0.8597751
## All.X.glmnet                                   25       0.8626593
## Max.cor.Y.rcv.1X1.Interact.glmnet              25       0.7919708
##                                   max.Sens.fit max.Spec.fit
## MFO.myMFO_classfr                    1.0000000    0.0000000
## Random.myrandom_classfr              0.5164234    0.4555085
## Max.cor.Y.rcv.1X1.glmnet             0.8339416    0.7457627
## Max.cor.Y.rcv.3X1.glmnet             0.8339416    0.7500000
## Max.cor.Y.rcv.3X3.glmnet             0.8339416    0.7500000
## Max.cor.Y.rcv.3X5.glmnet             0.8339416    0.7500000
## Max.cor.Y.rcv.5X1.glmnet             0.8412409    0.7500000
## Max.cor.Y.rcv.5X3.glmnet             0.8412409    0.7500000
## Max.cor.Y.rcv.5X5.glmnet             0.8448905    0.7478814
## Max.cor.Y.rcv.1X1.cp.0.rpart         0.8978102    0.7584746
## Max.cor.Y.rpart                      0.8959854    0.7245763
## Interact.High.cor.Y.glmnet           0.8886861    0.7055085
## Low.cor.X.glmnet                     0.8996350    0.8199153
## RFE.X.glmnet                         0.9032847    0.8220339
## RFE.X.glm                            0.8996350    0.8199153
## All.X.glmnet                         0.9032847    0.8220339
## Max.cor.Y.rcv.1X1.Interact.glmnet    0.8339416    0.7500000
##                                   max.AUCROCR.fit opt.prob.threshold.fit
## MFO.myMFO_classfr                       0.5000000                    0.4
## Random.myrandom_classfr                 0.4893217                    0.4
## Max.cor.Y.rcv.1X1.glmnet                0.8620600                    0.4
## Max.cor.Y.rcv.3X1.glmnet                0.8621335                    0.5
## Max.cor.Y.rcv.3X3.glmnet                0.8622147                    0.5
## Max.cor.Y.rcv.3X5.glmnet                0.8622069                    0.5
## Max.cor.Y.rcv.5X1.glmnet                0.8621064                    0.5
## Max.cor.Y.rcv.5X3.glmnet                0.8621064                    0.5
## Max.cor.Y.rcv.5X5.glmnet                0.8621064                    0.5
## Max.cor.Y.rcv.1X1.cp.0.rpart            0.8816304                    0.4
## Max.cor.Y.rpart                         0.8387008                    0.5
## Interact.High.cor.Y.glmnet              0.8639370                    0.4
## Low.cor.X.glmnet                        0.9329264                    0.5
## RFE.X.glmnet                            0.9355785                    0.4
## RFE.X.glm                               0.9392823                    0.4
## All.X.glmnet                            0.9355785                    0.4
## Max.cor.Y.rcv.1X1.Interact.glmnet       0.8622147                    0.5
##                                   max.f.score.fit max.Accuracy.fit
## MFO.myMFO_classfr                       0.6327078        0.4627451
## Random.myrandom_classfr                 0.6327078        0.4627451
## Max.cor.Y.rcv.1X1.glmnet                0.7698745        0.7843137
## Max.cor.Y.rcv.3X1.glmnet                0.7720829        0.7950911
## Max.cor.Y.rcv.3X3.glmnet                0.7720829        0.7954221
## Max.cor.Y.rcv.3X5.glmnet                0.7720829        0.7962655
## Max.cor.Y.rcv.5X1.glmnet                0.7754655        0.7990899
## Max.cor.Y.rcv.5X3.glmnet                0.7754655        0.7964364
## Max.cor.Y.rcv.5X5.glmnet                0.7758242        0.7970687
## Max.cor.Y.rcv.1X1.cp.0.rpart            0.8102109        0.8323529
## Max.cor.Y.rpart                         0.7853042        0.7964073
## Interact.High.cor.Y.glmnet              0.7724750        0.8026098
## Low.cor.X.glmnet                        0.8468271        0.8349657
## RFE.X.glmnet                            0.8503119        0.8303886
## RFE.X.glm                               0.8496868        0.8307135
## All.X.glmnet                            0.8503119        0.8303886
## Max.cor.Y.rcv.1X1.Interact.glmnet       0.7720829        0.7954221
##                                   max.Kappa.fit max.AUCpROC.OOB
## MFO.myMFO_classfr                     0.0000000       0.5000000
## Random.myrandom_classfr               0.0000000       0.5064252
## Max.cor.Y.rcv.1X1.glmnet              0.5669826       0.7665376
## Max.cor.Y.rcv.3X1.glmnet              0.5860164       0.7592654
## Max.cor.Y.rcv.3X3.glmnet              0.5866808       0.7603840
## Max.cor.Y.rcv.3X5.glmnet              0.5883400       0.7603840
## Max.cor.Y.rcv.5X1.glmnet              0.5944245       0.7615026
## Max.cor.Y.rcv.5X3.glmnet              0.5888026       0.7615026
## Max.cor.Y.rcv.5X5.glmnet              0.5901123       0.7641135
## Max.cor.Y.rcv.1X1.cp.0.rpart          0.6606905       0.7504950
## Max.cor.Y.rpart                       0.5852116       0.7445254
## Interact.High.cor.Y.glmnet            0.5988972       0.7551533
## Low.cor.X.glmnet                      0.6666179       0.7801532
## RFE.X.glmnet                          0.6576319       0.7835089
## RFE.X.glm                             0.6585198       0.7851882
## All.X.glmnet                          0.6576319       0.7835089
## Max.cor.Y.rcv.1X1.Interact.glmnet     0.5866808       0.7603840
##                                   max.Sens.OOB max.Spec.OOB
## MFO.myMFO_classfr                    1.0000000    0.0000000
## Random.myrandom_classfr              0.5480984    0.4647520
## Max.cor.Y.rcv.1X1.glmnet             0.8255034    0.7075718
## Max.cor.Y.rcv.3X1.glmnet             0.8187919    0.6997389
## Max.cor.Y.rcv.3X3.glmnet             0.8210291    0.6997389
## Max.cor.Y.rcv.3X5.glmnet             0.8210291    0.6997389
## Max.cor.Y.rcv.5X1.glmnet             0.8232662    0.6997389
## Max.cor.Y.rcv.5X3.glmnet             0.8232662    0.6997389
## Max.cor.Y.rcv.5X5.glmnet             0.8232662    0.7049608
## Max.cor.Y.rcv.1X1.cp.0.rpart         0.8456376    0.6553525
## Max.cor.Y.rpart                      0.8545861    0.6344648
## Interact.High.cor.Y.glmnet           0.8680089    0.6422977
## Low.cor.X.glmnet                     0.8187919    0.7415144
## RFE.X.glmnet                         0.8255034    0.7415144
## RFE.X.glm                            0.8210291    0.7493473
## All.X.glmnet                         0.8255034    0.7415144
## Max.cor.Y.rcv.1X1.Interact.glmnet    0.8210291    0.6997389
##                                   max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO.myMFO_classfr                       0.5000000                    0.4
## Random.myrandom_classfr                 0.4956046                    0.4
## Max.cor.Y.rcv.1X1.glmnet                0.8197236                    0.4
## Max.cor.Y.rcv.3X1.glmnet                0.8197353                    0.4
## Max.cor.Y.rcv.3X3.glmnet                0.8197470                    0.4
## Max.cor.Y.rcv.3X5.glmnet                0.8196593                    0.4
## Max.cor.Y.rcv.5X1.glmnet                0.8195600                    0.4
## Max.cor.Y.rcv.5X3.glmnet                0.8195600                    0.4
## Max.cor.Y.rcv.5X5.glmnet                0.8193790                    0.4
## Max.cor.Y.rcv.1X1.cp.0.rpart            0.7934621                    0.3
## Max.cor.Y.rpart                         0.7855854                    0.3
## Interact.High.cor.Y.glmnet              0.8189000                    0.3
## Low.cor.X.glmnet                        0.8727052                    0.3
## RFE.X.glmnet                            0.8721444                    0.3
## RFE.X.glm                               0.8700475                    0.5
## All.X.glmnet                            0.8721444                    0.3
## Max.cor.Y.rcv.1X1.Interact.glmnet       0.8197470                    0.4
##                                   max.f.score.OOB max.Accuracy.OOB
## MFO.myMFO_classfr                       0.6314922        0.4614458
## Random.myrandom_classfr                 0.6314922        0.4614458
## Max.cor.Y.rcv.1X1.glmnet                0.7535854        0.7722892
## Max.cor.Y.rcv.3X1.glmnet                0.7519182        0.7662651
## Max.cor.Y.rcv.3X3.glmnet                0.7525510        0.7662651
## Max.cor.Y.rcv.3X5.glmnet                0.7515924        0.7650602
## Max.cor.Y.rcv.5X1.glmnet                0.7506361        0.7638554
## Max.cor.Y.rcv.5X3.glmnet                0.7506361        0.7638554
## Max.cor.Y.rcv.5X5.glmnet                0.7512690        0.7638554
## Max.cor.Y.rcv.1X1.cp.0.rpart            0.7313997        0.7433735
## Max.cor.Y.rpart                         0.7394270        0.7698795
## Interact.High.cor.Y.glmnet              0.7522698        0.7698795
## Low.cor.X.glmnet                        0.7719715        0.7686747
## RFE.X.glmnet                            0.7654028        0.7614458
## RFE.X.glm                               0.7653333        0.7879518
## All.X.glmnet                            0.7654028        0.7614458
## Max.cor.Y.rcv.1X1.Interact.glmnet       0.7525510        0.7662651
##                                   max.Kappa.OOB inv.elapsedtime.everything
## MFO.myMFO_classfr                     0.0000000                 3.42465753
## Random.myrandom_classfr               0.0000000                 3.24675325
## Max.cor.Y.rcv.1X1.glmnet              0.5419399                 1.35135135
## Max.cor.Y.rcv.3X1.glmnet              0.5311363                 0.64683053
## Max.cor.Y.rcv.3X3.glmnet              0.5313109                 0.48123195
## Max.cor.Y.rcv.3X5.glmnet              0.5289828                 0.38940810
## Max.cor.Y.rcv.5X1.glmnet              0.5266555                 0.57836900
## Max.cor.Y.rcv.5X3.glmnet              0.5266555                 0.38505968
## Max.cor.Y.rcv.5X5.glmnet              0.5268317                 0.32144005
## Max.cor.Y.rcv.1X1.cp.0.rpart          0.4862697                 1.33333333
## Max.cor.Y.rpart                       0.5341327                 0.61274510
## Interact.High.cor.Y.glmnet            0.5374385                 0.36403349
## Low.cor.X.glmnet                      0.5411011                 0.31959092
## RFE.X.glmnet                          0.5269348                 0.06570734
## RFE.X.glm                             0.5720872                 0.34602076
## All.X.glmnet                          0.5269348                 0.06744908
## Max.cor.Y.rcv.1X1.Interact.glmnet     0.5313109                 0.43122035
##                                   inv.elapsedtime.final
## MFO.myMFO_classfr                            333.333333
## Random.myrandom_classfr                     1000.000000
## Max.cor.Y.rcv.1X1.glmnet                      43.478261
## Max.cor.Y.rcv.3X1.glmnet                      55.555556
## Max.cor.Y.rcv.3X3.glmnet                      62.500000
## Max.cor.Y.rcv.3X5.glmnet                      62.500000
## Max.cor.Y.rcv.5X1.glmnet                      66.666667
## Max.cor.Y.rcv.5X3.glmnet                      62.500000
## Max.cor.Y.rcv.5X5.glmnet                      66.666667
## Max.cor.Y.rcv.1X1.cp.0.rpart                  83.333333
## Max.cor.Y.rpart                               76.923077
## Interact.High.cor.Y.glmnet                    10.000000
## Low.cor.X.glmnet                               8.264463
## RFE.X.glmnet                                   1.872659
## RFE.X.glm                                      5.208333
## All.X.glmnet                                   1.834862
## Max.cor.Y.rcv.1X1.Interact.glmnet             62.500000
print(myplot_radar(radar_inp_df=plt_models_df))
## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors
## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 17. Consider specifying shapes manually if you must have them.
## Warning: Removed 220 rows containing missing values (geom_point).
## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors
## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 17. Consider specifying shapes manually if you must have them.

# print(myplot_radar(radar_inp_df=subset(plt_models_df, 
#         !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))

# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df, 
                max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
                min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
    # Does CI alredy exist ?
    var_components <- unlist(strsplit(var, "SD"))
    varActul <- paste0(var_components[1],          var_components[2])
    varUpper <- paste0(var_components[1], "Upper", var_components[2])
    varLower <- paste0(var_components[1], "Lower", var_components[2])
    if (varUpper %in% names(glb_models_df)) {
        warning(varUpper, " already exists in glb_models_df")
        # Assuming Lower also exists
        next
    }    
    print(sprintf("var:%s", var))
    # CI is dependent on sample size in t distribution; df=n-1
    glb_models_df[, varUpper] <- glb_models_df[, varActul] + 
        glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
    glb_models_df[, varLower] <- glb_models_df[, varActul] - 
        glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}
## Warning: max.AccuracyUpper.fit already exists in glb_models_df
## [1] "var:max.KappaSD.fit"
# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
    var_components <- unlist(strsplit(var, "Upper"))
    col_name <- unlist(paste(var_components, collapse=""))
    plt_models_df[, col_name] <- glb_models_df[, col_name]
    for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
        pltCI_models_df[, name] <- glb_models_df[, name]
}

build_statsCI_data <- function(plt_models_df) {
    mltd_models_df <- melt(plt_models_df, id.vars="id")
    mltd_models_df$data <- sapply(1:nrow(mltd_models_df), 
        function(row_ix) tail(unlist(strsplit(as.character(
            mltd_models_df[row_ix, "variable"]), "[.]")), 1))
    mltd_models_df$label <- sapply(1:nrow(mltd_models_df), 
        function(row_ix) head(unlist(strsplit(as.character(
            mltd_models_df[row_ix, "variable"]), 
            paste0(".", mltd_models_df[row_ix, "data"]))), 1))
    #print(mltd_models_df)
    
    return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)

mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
    for (type in c("Upper", "Lower")) {
        if (length(var_components <- unlist(strsplit(
                as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
            #print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
            mltdCI_models_df[row_ix, "label"] <- var_components[1]
            mltdCI_models_df[row_ix, "data"] <- 
                unlist(strsplit(var_components[2], "[.]"))[2]
            mltdCI_models_df[row_ix, "type"] <- type
            break
        }
    }    
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable), 
                            timevar="type", 
        idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")), 
                            direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)

# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
    for (type in unique(mltd_models_df$data)) {
        var_type <- paste0(var, ".", type)
        # if this data is already present, next
        if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
                                       sep=".")))
            next
        #print(sprintf("var_type:%s", var_type))
        goback_vars <- c(goback_vars, var_type)
    }
}

if (length(goback_vars) > 0) {
    mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
    mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}

# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")], 
#                         all.x=TRUE)

png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") + 
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") + 
        geom_errorbar(data=mrgdCI_models_df, 
            mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) + 
          facet_grid(label ~ data, scales="free") + 
          theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
## Warning: Removed 5 rows containing missing values (geom_errorbar).
dev.off()
## quartz_off_screen 
##                 2
print(gp)
## Warning: Removed 5 rows containing missing values (geom_errorbar).

dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
# if (glb_is_classification && glb_is_binomial) 
#     dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols])
##                                   id max.Accuracy.OOB max.AUCROCR.OOB
## 15                         RFE.X.glm        0.7879518       0.8700475
## 3           Max.cor.Y.rcv.1X1.glmnet        0.7722892       0.8197236
## 12        Interact.High.cor.Y.glmnet        0.7698795       0.8189000
## 11                   Max.cor.Y.rpart        0.7698795       0.7855854
## 13                  Low.cor.X.glmnet        0.7686747       0.8727052
## 5           Max.cor.Y.rcv.3X3.glmnet        0.7662651       0.8197470
## 17 Max.cor.Y.rcv.1X1.Interact.glmnet        0.7662651       0.8197470
## 4           Max.cor.Y.rcv.3X1.glmnet        0.7662651       0.8197353
## 6           Max.cor.Y.rcv.3X5.glmnet        0.7650602       0.8196593
## 7           Max.cor.Y.rcv.5X1.glmnet        0.7638554       0.8195600
## 8           Max.cor.Y.rcv.5X3.glmnet        0.7638554       0.8195600
## 9           Max.cor.Y.rcv.5X5.glmnet        0.7638554       0.8193790
## 14                      RFE.X.glmnet        0.7614458       0.8721444
## 16                      All.X.glmnet        0.7614458       0.8721444
## 10      Max.cor.Y.rcv.1X1.cp.0.rpart        0.7433735       0.7934621
## 1                  MFO.myMFO_classfr        0.4614458       0.5000000
## 2            Random.myrandom_classfr        0.4614458       0.4956046
##    max.AUCpROC.OOB max.Accuracy.fit opt.prob.threshold.fit
## 15       0.7851882        0.8307135                    0.4
## 3        0.7665376        0.7843137                    0.4
## 12       0.7551533        0.8026098                    0.4
## 11       0.7445254        0.7964073                    0.5
## 13       0.7801532        0.8349657                    0.5
## 5        0.7603840        0.7954221                    0.5
## 17       0.7603840        0.7954221                    0.5
## 4        0.7592654        0.7950911                    0.5
## 6        0.7603840        0.7962655                    0.5
## 7        0.7615026        0.7990899                    0.5
## 8        0.7615026        0.7964364                    0.5
## 9        0.7641135        0.7970687                    0.5
## 14       0.7835089        0.8303886                    0.4
## 16       0.7835089        0.8303886                    0.4
## 10       0.7504950        0.8323529                    0.4
## 1        0.5000000        0.4627451                    0.4
## 2        0.5064252        0.4627451                    0.4
##    opt.prob.threshold.OOB
## 15                    0.5
## 3                     0.4
## 12                    0.3
## 11                    0.3
## 13                    0.3
## 5                     0.4
## 17                    0.4
## 4                     0.4
## 6                     0.4
## 7                     0.4
## 8                     0.4
## 9                     0.4
## 14                    0.3
## 16                    0.3
## 10                    0.3
## 1                     0.4
## 2                     0.4
print(myplot_radar(radar_inp_df = dsp_models_df))
## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors
## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 17. Consider specifying shapes manually if you must have them.
## Warning: Removed 77 rows containing missing values (geom_point).
## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors
## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 17. Consider specifying shapes manually if you must have them.

print("Metrics used for model selection:"); print(get_model_sel_frmla())
## [1] "Metrics used for model selection:"
## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB - max.Accuracy.fit - 
##     opt.prob.threshold.OOB
## <environment: 0x7fc2f049ec30>
print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))
## [1] "Best model id: RFE.X.glm"
glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL, verbose=FALSE) {
    mdl <- glb_models_lst[[mdl_id]]
    #rsp_var_out <- paste0(rsp_var_out, mdl_id)

    rsp_var_out <- paste0(glb_rsp_var, ".predict.")
    predct_var_name <- paste0(rsp_var_out, mdl_id)        
    predct_prob_var_name <- paste0(rsp_var_out, mdl_id, ".prob")    
    predct_accurate_var_name <- paste0(rsp_var_out, mdl_id, ".accurate")
    predct_error_var_name <- paste0(rsp_var_out, mdl_id, ".err")
    predct_erabs_var_name <- paste0(rsp_var_out, mdl_id, ".err.abs")

    if (glb_is_regression) {
        df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
                  facet_wrap(reformulate(glb_category_var), scales = "free") + 
                  stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
        if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
                  #facet_wrap(reformulate(glb_category_var), scales = "free") + 
                  stat_smooth(method="auto"))
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
                  #facet_wrap(reformulate(glb_category_var), scales = "free") + 
                  stat_smooth(method="glm"))
        
        df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }

    if (glb_is_classification && glb_is_binomial) {
        prob_threshold <- glb_models_df[glb_models_df$id == mdl_id, 
                                        "opt.prob.threshold.OOB"]
        if (is.null(prob_threshold) || is.na(prob_threshold)) {
            warning("Using default probability threshold: ", prob_threshold_def)
            if (is.null(prob_threshold <- prob_threshold_def))
                stop("Default probability threshold is NULL")
        }
        
        df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
        df[, predct_var_name] <- 
                factor(levels(df[, glb_rsp_var])[
                    (df[, predct_prob_var_name] >=
                        prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
    
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
#                   facet_wrap(reformulate(glb_category_var), scales = "free") + 
#                   stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
#         if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glb_category_var), scales = "free") + 
#                   stat_smooth(method="auto"))
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glb_category_var), scales = "free") + 
#                   stat_smooth(method="glm"))
        
        # if prediction is a TP (true +ve), measure distance from 1.0
        tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
        #rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glb_id_var, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a TN (true -ve), measure distance from 0.0
        tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
        #rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glb_id_var, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FP (flse +ve), measure distance from 0.0
        fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
        #rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glb_id_var, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FN (flse -ve), measure distance from 1.0
        fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
        #rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glb_id_var, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]

        
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }    
    
    if (glb_is_classification && !glb_is_binomial) {
        df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
        df[, paste0(predct_var_name, ".prob")] <- 
            predict(mdl, newdata = df, type = "prob")
        stop("Multinomial prediction error calculation needs to be implemented...")
    }

    return(df)
}    

#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df; glb_trnobs_df <- sav_trnobs_df; glb_fitobs_df <- sav_fitobs_df; glb_OOBobs_df <- sav_OOBobs_df; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df    

myget_category_stats <- function(obs_df, mdl_id, label) {
    require(dplyr)
    require(lazyeval)
    
    predct_var_name <- paste0(glb_rsp_var_out, mdl_id)        
    predct_error_var_name <- paste0(glb_rsp_var_out, mdl_id, ".err.abs")
    
    if (!predct_var_name %in% names(obs_df))
        obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var_out)
    
    tmp_obs_df <- obs_df %>%
        dplyr::select_(glb_category_var, glb_rsp_var, predct_var_name, predct_error_var_name) 
    #dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
    names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
    
    ret_ctgry_df <- tmp_obs_df %>%
        dplyr::group_by_(glb_category_var) %>%
        dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)), 
            interp(~sum(var), var=as.name(paste0("err.abs.", label))), 
            interp(~mean(var), var=as.name(paste0("err.abs.", label))),
            interp(~n()))
    names(ret_ctgry_df) <- c(glb_category_var, 
                             #paste0(glb_rsp_var, ".abs.", label, ".sum"),
                             paste0("err.abs.", label, ".sum"),                             
                             paste0("err.abs.", label, ".mean"), 
                             paste0(".n.", label))
    ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
    #colSums(ret_ctgry_df[, -grep(glb_category_var, names(ret_ctgry_df))])
    
    return(ret_ctgry_df)    
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glb_fitobs_df, mdl_id="", label="fit"))[, -grep(glb_category_var, names(ctgry_df))]))

if (!is.null(glb_mdl_ensemble)) {
    mdl_id_pfx <- "Ensemble"

    if (#(glb_is_regression) | 
        ((glb_is_classification) & (!glb_is_binomial)))
        stop("Ensemble models not implemented yet for multinomial classification")
    
    if (glb_mdl_ensemble == "auto") {
        mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")
        tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
        row.names(tmp_models_df) <- tmp_models_df$id
    #     mdl_threshold_pos <- min(which(tmp_models_df$id %in% 
    #                                 c("MFO.myMFO_classfr", "Baseline.mybaseln_classfr"))) - 1
        mdl_threshold_pos <- 
            min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
        glb_mdl_ensemble <- tmp_models_df$id[1:mdl_threshold_pos]
    }
    
    for (mdl_id in glb_mdl_ensemble) {
        if (!(mdl_id %in% names(glb_models_lst))) {
            warning("Model ", mdl_id, " in glb_model_ensemble not found !")
            next
        }
        glb_fitobs_df <- glb_get_predictions(df = glb_fitobs_df, mdl_id,
                                             glb_rsp_var_out)
        glb_OOBobs_df <- glb_get_predictions(df = glb_OOBobs_df, mdl_id,
                                             glb_rsp_var_out)
    }
    
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(glb_rsp_var_out, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
    
#cor_df <- data.frame(cor=cor(glb_fitobs_df[, glb_rsp_var], glb_fitobs_df[, paste(glb_rsp_var_out, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glb_fitobs_df <- glb_get_predictions(df=glb_fitobs_df, "Ensemble.glmnet", glb_rsp_var_out);print(colSums((ctgry_df <- myget_category_stats(obs_df=glb_fitobs_df, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glb_category_var, names(ctgry_df))]))
    
    ### bid0_sp
    #  Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
    #  old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
    #  RFE only ;       models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
    #  RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
    #  RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
    #  RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
    #  RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
    #  RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
    #  RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
    ### bid0_sp
    ### bid1_sp
    # "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
    # "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
    ### bid1_sp

    indep_vars <- paste(glb_rsp_var_out, glb_mdl_ensemble, sep = "")
    if (glb_is_classification)
        indep_vars <- paste(indep_vars, ".prob", sep = "")
    # Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
    indep_vars <- intersect(indep_vars, names(glb_fitobs_df))
    
#     indep_vars <- grep(glb_rsp_var_out, names(glb_fitobs_df), fixed=TRUE, value=TRUE)
#     if (glb_is_regression)
#         indep_vars <- indep_vars[!grepl("(err\\.abs|accurate)$", indep_vars)]
#     if (glb_is_classification && glb_is_binomial)
#         indep_vars <- grep("prob$", indep_vars, value=TRUE) else
#         indep_vars <- indep_vars[!grepl("err$", indep_vars)]

    #rfe_fit_ens_results <- myrun_rfe(glb_fitobs_df, indep_vars)
    
    for (method in c("glm", "glmnet")) {
        for (trainControlMethod in 
             c("boot", "boot632", "cv", "repeatedcv"
               #, "LOOCV" # tuneLength * nrow(fitDF)
               , "LGOCV", "adaptive_cv"
               #, "adaptive_boot"  #error: adaptive$min should be less than 3 
               #, "adaptive_LGOCV" #error: adaptive$min should be less than 3 
               )) {
            #sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
            #glb_models_df <- sav_models_df; print(glb_models_df$id)
                
            if ((method == "glm") && (trainControlMethod != "repeatedcv"))
                # glm used only to identify outliers
                next
            
            ret_lst <- myfit_mdl(
                mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                    id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod), 
                    type = glb_model_type, tune.df = NULL,
                    trainControl.method = trainControlMethod,
                    trainControl.number = glb_rcv_n_folds,
                    trainControl.repeats = glb_rcv_n_repeats,
                    trainControl.classProbs = glb_is_classification,
                    trainControl.summaryFunction = glbMdlMetricSummaryFn,
                    train.metric = glbMdlMetricSummary, 
                    train.maximize = glbMdlMetricMaximize,    
                    train.method = method)),
                indep_vars = indep_vars, rsp_var = glb_rsp_var, 
                fit_df = glb_fitobs_df, OOB_df = glb_OOBobs_df)
        }
    }
    dsp_models_df <- get_dsp_models_df()
}

if (is.null(glb_sel_mdl_id)) 
    glb_sel_mdl_id <- dsp_models_df[1, "id"] else 
    print(sprintf("User specified selection: %s", glb_sel_mdl_id))   
## [1] "User specified selection: All.X.glmnet"
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])

##             Length Class      Mode     
## a0           100   -none-     numeric  
## beta        9300   dgCMatrix  S4       
## df           100   -none-     numeric  
## dim            2   -none-     numeric  
## lambda       100   -none-     numeric  
## dev.ratio    100   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## classnames     2   -none-     character
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        93   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      2   -none-     character
## [1] "min lambda > lambdaOpt:"
##                              (Intercept) 
##                             5.9298599950 
##                                   .rnorm 
##                            -0.0367487387 
##                             D.chrs.n.log 
##                            -0.2852958123 
##                      D.chrs.pnct11.n.log 
##                             0.4714450805 
##                      D.chrs.pnct13.n.log 
##                            -0.1653784579 
##                        D.chrs.uppr.n.log 
##                            -0.0116026565 
##                D.ratio.weight.sum.wrds.n 
##                            -0.0593199934 
##               D.ratio.wrds.stop.n.wrds.n 
##                            -2.9071388332 
##                  D.terms.post.stem.n.log 
##                            -0.2864766681 
##                  D.terms.post.stop.n.log 
##                            -0.9265313808 
##                   D.weight.post.stem.sum 
##                            -0.1209650771 
##                             D.weight.sum 
##                            -0.1180951219 
##             D.weight.sum.stem.stop.Ratio 
##                            -2.9013605803 
##                             D.wrds.n.log 
##                             1.2607015439 
##                        D.wrds.stop.n.log 
##                             0.1152103538 
##                         D.wrds.unq.n.log 
##                            -0.1770678274 
##                                 biddable 
##                             2.5343114368 
##                     cellular.fctrUnknown 
##                            -0.7922522457 
##                           color.fctrGold 
##                             0.0871372337 
##                     color.fctrSpace Gray 
##                            -0.3474018561 
##                        color.fctrUnknown 
##                             0.3560862355 
##                          color.fctrWhite 
##                            -0.2253657549 
##   condition.fctrFor parts or not working 
##                            -0.7365978083 
##   condition.fctrManufacturer refurbished 
##                            -1.1969859100 
##                        condition.fctrNew 
##                             0.0297417566 
##    condition.fctrNew other (see details) 
##                             0.7229925582 
##         condition.fctrSeller refurbished 
##                            -1.0271638293 
##                        prdl.my.fctriPad1 
##                            -0.6856562063 
##                        prdl.my.fctriPad2 
##                            -0.4622200902 
##                        prdl.my.fctriPad3 
##                             0.1837795731 
##                        prdl.my.fctriPad4 
##                             1.4053112779 
##                      prdl.my.fctriPadAir 
##                             1.5486082406 
##                     prdl.my.fctriPadAir2 
##                             1.9064724435 
##                     prdl.my.fctriPadmini 
##                            -0.4755225796 
##                    prdl.my.fctriPadmini2 
##                             1.3324979842 
##                    prdl.my.fctriPadmini3 
##                             0.7573021470 
##                spdiff.cut.fctr(-100,-10] 
##                             2.3577551693 
##                  spdiff.cut.fctr(-10,-1] 
##                             4.1547129846 
##                    spdiff.cut.fctr(-1,0] 
##                             5.3348860714 
##                     spdiff.cut.fctr(0,1] 
##                             4.4072812301 
##                    spdiff.cut.fctr(1,10] 
##                             4.4073453219 
##                  spdiff.cut.fctr(10,100] 
##                             3.8533894456 
##               spdiff.cut.fctr(100,1e+03] 
##                             1.0918155713 
##                           sprice.d20nexp 
##                            -1.5657841541 
##                             sprice.log10 
##                            -0.5769819282 
##                             sprice.root2 
##                            -0.1085201562 
##                      startprice.dcm1.is9 
##                            -0.4894970169 
##                      startprice.dcm2.is9 
##                            -0.0838883551 
##                      startprice.dgt1.is9 
##                             0.0254181773 
##                      startprice.dgt2.is9 
##                             0.1157259996 
##                           storage.fctr16 
##                            -0.1300313626 
##                           storage.fctr32 
##                             0.0005589105 
##                           storage.fctr64 
##                             0.2180851783 
##                      storage.fctrUnknown 
##                             1.7336441556 
##          cellular.fctr0:carrier.fctrNone 
##                             0.1706810167 
##         cellular.fctr1:carrier.fctrOther 
##                             2.6072585524 
##        cellular.fctr1:carrier.fctrSprint 
##                             1.0270604082 
##      cellular.fctr1:carrier.fctrT-Mobile 
##                            -0.2821763037 
##       cellular.fctr1:carrier.fctrUnknown 
##                            -0.0179172973 
## cellular.fctrUnknown:carrier.fctrUnknown 
##                            -0.7883348835 
##       cellular.fctr1:carrier.fctrVerizon 
##                             0.3156224380 
##     prdl.my.fctrUnknown:.clusterid.fctr2 
##                             0.8932344300 
##       prdl.my.fctriPad1:.clusterid.fctr2 
##                            -0.7404516345 
##       prdl.my.fctriPad3:.clusterid.fctr2 
##                             0.2701330403 
##       prdl.my.fctriPad4:.clusterid.fctr2 
##                            -1.4552193885 
##     prdl.my.fctriPadAir:.clusterid.fctr2 
##                            -0.3279730199 
##   prdl.my.fctriPadmini2:.clusterid.fctr2 
##                            -0.6287767988 
##     prdl.my.fctrUnknown:.clusterid.fctr3 
##                            -0.7598398151 
##       prdl.my.fctriPad1:.clusterid.fctr3 
##                            -1.1337789530 
##       prdl.my.fctriPad4:.clusterid.fctr3 
##                            -5.9466674332 
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
##  [1] "(Intercept)"                              
##  [2] ".rnorm"                                   
##  [3] "D.chrs.n.log"                             
##  [4] "D.chrs.pnct11.n.log"                      
##  [5] "D.chrs.pnct13.n.log"                      
##  [6] "D.chrs.uppr.n.log"                        
##  [7] "D.ratio.weight.sum.wrds.n"                
##  [8] "D.ratio.wrds.stop.n.wrds.n"               
##  [9] "D.terms.post.stem.n.log"                  
## [10] "D.terms.post.stop.n.log"                  
## [11] "D.weight.post.stem.sum"                   
## [12] "D.weight.post.stop.sum"                   
## [13] "D.weight.sum"                             
## [14] "D.weight.sum.stem.stop.Ratio"             
## [15] "D.wrds.n.log"                             
## [16] "D.wrds.stop.n.log"                        
## [17] "D.wrds.unq.n.log"                         
## [18] "biddable"                                 
## [19] "cellular.fctr1"                           
## [20] "cellular.fctrUnknown"                     
## [21] "color.fctrGold"                           
## [22] "color.fctrSpace Gray"                     
## [23] "color.fctrUnknown"                        
## [24] "color.fctrWhite"                          
## [25] "condition.fctrFor parts or not working"   
## [26] "condition.fctrManufacturer refurbished"   
## [27] "condition.fctrNew"                        
## [28] "condition.fctrNew other (see details)"    
## [29] "condition.fctrSeller refurbished"         
## [30] "prdl.my.fctriPad1"                        
## [31] "prdl.my.fctriPad2"                        
## [32] "prdl.my.fctriPad3"                        
## [33] "prdl.my.fctriPad4"                        
## [34] "prdl.my.fctriPadAir"                      
## [35] "prdl.my.fctriPadAir2"                     
## [36] "prdl.my.fctriPadmini"                     
## [37] "prdl.my.fctriPadmini2"                    
## [38] "prdl.my.fctriPadmini3"                    
## [39] "spdiff.cut.fctr(-100,-10]"                
## [40] "spdiff.cut.fctr(-10,-1]"                  
## [41] "spdiff.cut.fctr(-1,0]"                    
## [42] "spdiff.cut.fctr(0,1]"                     
## [43] "spdiff.cut.fctr(1,10]"                    
## [44] "spdiff.cut.fctr(10,100]"                  
## [45] "spdiff.cut.fctr(100,1e+03]"               
## [46] "sprice.d20nexp"                           
## [47] "sprice.log10"                             
## [48] "sprice.root2"                             
## [49] "startprice.dcm1.is9"                      
## [50] "startprice.dcm2.is9"                      
## [51] "startprice.dgt1.is9"                      
## [52] "startprice.dgt2.is9"                      
## [53] "storage.fctr16"                           
## [54] "storage.fctr32"                           
## [55] "storage.fctr64"                           
## [56] "storage.fctrUnknown"                      
## [57] "cellular.fctr0:carrier.fctrNone"          
## [58] "cellular.fctr1:carrier.fctrNone"          
## [59] "cellular.fctrUnknown:carrier.fctrNone"    
## [60] "cellular.fctr0:carrier.fctrOther"         
## [61] "cellular.fctr1:carrier.fctrOther"         
## [62] "cellular.fctrUnknown:carrier.fctrOther"   
## [63] "cellular.fctr0:carrier.fctrSprint"        
## [64] "cellular.fctr1:carrier.fctrSprint"        
## [65] "cellular.fctrUnknown:carrier.fctrSprint"  
## [66] "cellular.fctr0:carrier.fctrT-Mobile"      
## [67] "cellular.fctr1:carrier.fctrT-Mobile"      
## [68] "cellular.fctrUnknown:carrier.fctrT-Mobile"
## [69] "cellular.fctr0:carrier.fctrUnknown"       
## [70] "cellular.fctr1:carrier.fctrUnknown"       
## [71] "cellular.fctrUnknown:carrier.fctrUnknown" 
## [72] "cellular.fctr0:carrier.fctrVerizon"       
## [73] "cellular.fctr1:carrier.fctrVerizon"       
## [74] "cellular.fctrUnknown:carrier.fctrVerizon" 
## [75] "prdl.my.fctrUnknown:.clusterid.fctr2"     
## [76] "prdl.my.fctriPad1:.clusterid.fctr2"       
## [77] "prdl.my.fctriPad2:.clusterid.fctr2"       
## [78] "prdl.my.fctriPad3:.clusterid.fctr2"       
## [79] "prdl.my.fctriPad4:.clusterid.fctr2"       
## [80] "prdl.my.fctriPadAir:.clusterid.fctr2"     
## [81] "prdl.my.fctriPadAir2:.clusterid.fctr2"    
## [82] "prdl.my.fctriPadmini:.clusterid.fctr2"    
## [83] "prdl.my.fctriPadmini2:.clusterid.fctr2"   
## [84] "prdl.my.fctriPadmini3:.clusterid.fctr2"   
## [85] "prdl.my.fctrUnknown:.clusterid.fctr3"     
## [86] "prdl.my.fctriPad1:.clusterid.fctr3"       
## [87] "prdl.my.fctriPad2:.clusterid.fctr3"       
## [88] "prdl.my.fctriPad3:.clusterid.fctr3"       
## [89] "prdl.my.fctriPad4:.clusterid.fctr3"       
## [90] "prdl.my.fctriPadAir:.clusterid.fctr3"     
## [91] "prdl.my.fctriPadAir2:.clusterid.fctr3"    
## [92] "prdl.my.fctriPadmini:.clusterid.fctr3"    
## [93] "prdl.my.fctriPadmini2:.clusterid.fctr3"   
## [94] "prdl.my.fctriPadmini3:.clusterid.fctr3"
## [1] TRUE
#stop(here"); glb_to_sav()
# From here to save(), this should all be in one function
#   these are executed in the same seq twice more:
#       fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glb_sel_mdl_id))
## [1] "All.X.glmnet fit prediction diagnostics:"
glb_fitobs_df <- glb_get_predictions(df=glb_fitobs_df, mdl_id=glb_sel_mdl_id, 
                                     rsp_var_out=glb_rsp_var_out)
print(sprintf("%s OOB prediction diagnostics:", glb_sel_mdl_id))
## [1] "All.X.glmnet OOB prediction diagnostics:"
glb_OOBobs_df <- glb_get_predictions(df = glb_OOBobs_df, mdl_id = glb_sel_mdl_id, 
                                     rsp_var_out = glb_rsp_var_out)

glb_featsimp_df <- 
    myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".importance")] <- glb_featsimp_df$importance; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.importance) | (abs(RFE.X.YeoJohnson.glmnet.importance - RFE.X.glmnet.importance) > 0.0001), select=-importance)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))
print(glb_featsimp_df)
##                                           importance
## spdiff.cut.fctr(-1,0]                      100.00000
## spdiff.cut.fctr(1,10]                       91.77825
## spdiff.cut.fctr(0,1]                        91.77769
## spdiff.cut.fctr(-10,-1]                     89.53891
## spdiff.cut.fctr(10,100]                     86.86797
## cellular.fctr1:carrier.fctrOther            75.82223
## biddable                                    75.17563
## spdiff.cut.fctr(-100,-10]                   73.61063
## prdl.my.fctriPadAir2                        69.61045
## storage.fctrUnknown                         68.07849
## prdl.my.fctriPadAir                         66.43833
## prdl.my.fctriPad4                           65.16814
## prdl.my.fctriPadmini2                       64.52272
## D.wrds.n.log                                63.88632
## spdiff.cut.fctr(100,1e+03]                  62.38931
## cellular.fctr1:carrier.fctrSprint           61.81532
## prdl.my.fctrUnknown:.clusterid.fctr2        60.62908
## prdl.my.fctriPadmini3                       59.42417
## condition.fctrNew other (see details)       59.12005
## D.chrs.pnct11.n.log                         56.89033
## color.fctrUnknown                           55.86778
## cellular.fctr1:carrier.fctrVerizon          55.50911
## prdl.my.fctriPad3:.clusterid.fctr2          55.10589
## storage.fctr64                              54.64454
## prdl.my.fctriPad3                           54.34045
## cellular.fctr0:carrier.fctrNone             54.22434
## startprice.dgt2.is9                         53.73722
## D.wrds.stop.n.log                           53.73265
## color.fctrGold                              53.48381
## condition.fctrNew                           52.97506
## startprice.dgt1.is9                         52.93673
## storage.fctr32                              52.71638
## D.weight.post.stop.sum                      52.71142
## cellular.fctr1                              52.71142
## cellular.fctr1:carrier.fctrNone             52.71142
## cellular.fctrUnknown:carrier.fctrNone       52.71142
## cellular.fctr0:carrier.fctrOther            52.71142
## cellular.fctrUnknown:carrier.fctrOther      52.71142
## cellular.fctr0:carrier.fctrSprint           52.71142
## cellular.fctrUnknown:carrier.fctrSprint     52.71142
## cellular.fctr0:carrier.fctrT-Mobile         52.71142
## cellular.fctrUnknown:carrier.fctrT-Mobile   52.71142
## cellular.fctr0:carrier.fctrUnknown          52.71142
## cellular.fctr0:carrier.fctrVerizon          52.71142
## cellular.fctrUnknown:carrier.fctrVerizon    52.71142
## prdl.my.fctriPad2:.clusterid.fctr2          52.71142
## prdl.my.fctriPadAir2:.clusterid.fctr2       52.71142
## prdl.my.fctriPadmini:.clusterid.fctr2       52.71142
## prdl.my.fctriPadmini3:.clusterid.fctr2      52.71142
## prdl.my.fctriPad2:.clusterid.fctr3          52.71142
## prdl.my.fctriPad3:.clusterid.fctr3          52.71142
## prdl.my.fctriPadAir:.clusterid.fctr3        52.71142
## prdl.my.fctriPadAir2:.clusterid.fctr3       52.71142
## prdl.my.fctriPadmini:.clusterid.fctr3       52.71142
## prdl.my.fctriPadmini2:.clusterid.fctr3      52.71142
## prdl.my.fctriPadmini3:.clusterid.fctr3      52.71142
## D.chrs.uppr.n.log                           52.60858
## cellular.fctr1:carrier.fctrUnknown          52.55260
## .rnorm                                      52.38568
## D.ratio.weight.sum.wrds.n                   52.18561
## startprice.dcm2.is9                         51.96783
## sprice.root2                                51.74950
## D.weight.sum                                51.66462
## D.weight.post.stem.sum                      51.63919
## storage.fctr16                              51.55882
## D.chrs.pnct13.n.log                         51.24550
## D.wrds.unq.n.log                            51.14189
## color.fctrWhite                             50.71378
## cellular.fctr1:carrier.fctrT-Mobile         50.21020
## D.chrs.n.log                                50.18255
## D.terms.post.stem.n.log                     50.17209
## prdl.my.fctriPadAir:.clusterid.fctr2        49.80426
## color.fctrSpace Gray                        49.63204
## prdl.my.fctriPad2                           48.61429
## prdl.my.fctriPadmini                        48.49638
## startprice.dcm1.is9                         48.37251
## sprice.log10                                47.59704
## prdl.my.fctriPadmini2:.clusterid.fctr2      47.13793
## prdl.my.fctriPad1                           46.63375
## condition.fctrFor parts or not working      46.18220
## prdl.my.fctriPad1:.clusterid.fctr2          46.14804
## prdl.my.fctrUnknown:.clusterid.fctr3        45.97618
## cellular.fctrUnknown:carrier.fctrUnknown    45.72360
## cellular.fctrUnknown                        45.68888
## D.terms.post.stop.n.log                     44.49862
## condition.fctrSeller refurbished            43.60662
## prdl.my.fctriPad1:.clusterid.fctr3          42.66158
## condition.fctrManufacturer refurbished      42.10131
## prdl.my.fctriPad4:.clusterid.fctr2          39.81232
## sprice.d20nexp                              38.83227
## D.weight.sum.stem.stop.Ratio                26.99368
## D.ratio.wrds.stop.n.wrds.n                  26.94246
## prdl.my.fctriPad4:.clusterid.fctr3           0.00000
##                                           All.X.glmnet.importance
## spdiff.cut.fctr(-1,0]                                   100.00000
## spdiff.cut.fctr(1,10]                                    91.77825
## spdiff.cut.fctr(0,1]                                     91.77769
## spdiff.cut.fctr(-10,-1]                                  89.53891
## spdiff.cut.fctr(10,100]                                  86.86797
## cellular.fctr1:carrier.fctrOther                         75.82223
## biddable                                                 75.17563
## spdiff.cut.fctr(-100,-10]                                73.61063
## prdl.my.fctriPadAir2                                     69.61045
## storage.fctrUnknown                                      68.07849
## prdl.my.fctriPadAir                                      66.43833
## prdl.my.fctriPad4                                        65.16814
## prdl.my.fctriPadmini2                                    64.52272
## D.wrds.n.log                                             63.88632
## spdiff.cut.fctr(100,1e+03]                               62.38931
## cellular.fctr1:carrier.fctrSprint                        61.81532
## prdl.my.fctrUnknown:.clusterid.fctr2                     60.62908
## prdl.my.fctriPadmini3                                    59.42417
## condition.fctrNew other (see details)                    59.12005
## D.chrs.pnct11.n.log                                      56.89033
## color.fctrUnknown                                        55.86778
## cellular.fctr1:carrier.fctrVerizon                       55.50911
## prdl.my.fctriPad3:.clusterid.fctr2                       55.10589
## storage.fctr64                                           54.64454
## prdl.my.fctriPad3                                        54.34045
## cellular.fctr0:carrier.fctrNone                          54.22434
## startprice.dgt2.is9                                      53.73722
## D.wrds.stop.n.log                                        53.73265
## color.fctrGold                                           53.48381
## condition.fctrNew                                        52.97506
## startprice.dgt1.is9                                      52.93673
## storage.fctr32                                           52.71638
## D.weight.post.stop.sum                                   52.71142
## cellular.fctr1                                           52.71142
## cellular.fctr1:carrier.fctrNone                          52.71142
## cellular.fctrUnknown:carrier.fctrNone                    52.71142
## cellular.fctr0:carrier.fctrOther                         52.71142
## cellular.fctrUnknown:carrier.fctrOther                   52.71142
## cellular.fctr0:carrier.fctrSprint                        52.71142
## cellular.fctrUnknown:carrier.fctrSprint                  52.71142
## cellular.fctr0:carrier.fctrT-Mobile                      52.71142
## cellular.fctrUnknown:carrier.fctrT-Mobile                52.71142
## cellular.fctr0:carrier.fctrUnknown                       52.71142
## cellular.fctr0:carrier.fctrVerizon                       52.71142
## cellular.fctrUnknown:carrier.fctrVerizon                 52.71142
## prdl.my.fctriPad2:.clusterid.fctr2                       52.71142
## prdl.my.fctriPadAir2:.clusterid.fctr2                    52.71142
## prdl.my.fctriPadmini:.clusterid.fctr2                    52.71142
## prdl.my.fctriPadmini3:.clusterid.fctr2                   52.71142
## prdl.my.fctriPad2:.clusterid.fctr3                       52.71142
## prdl.my.fctriPad3:.clusterid.fctr3                       52.71142
## prdl.my.fctriPadAir:.clusterid.fctr3                     52.71142
## prdl.my.fctriPadAir2:.clusterid.fctr3                    52.71142
## prdl.my.fctriPadmini:.clusterid.fctr3                    52.71142
## prdl.my.fctriPadmini2:.clusterid.fctr3                   52.71142
## prdl.my.fctriPadmini3:.clusterid.fctr3                   52.71142
## D.chrs.uppr.n.log                                        52.60858
## cellular.fctr1:carrier.fctrUnknown                       52.55260
## .rnorm                                                   52.38568
## D.ratio.weight.sum.wrds.n                                52.18561
## startprice.dcm2.is9                                      51.96783
## sprice.root2                                             51.74950
## D.weight.sum                                             51.66462
## D.weight.post.stem.sum                                   51.63919
## storage.fctr16                                           51.55882
## D.chrs.pnct13.n.log                                      51.24550
## D.wrds.unq.n.log                                         51.14189
## color.fctrWhite                                          50.71378
## cellular.fctr1:carrier.fctrT-Mobile                      50.21020
## D.chrs.n.log                                             50.18255
## D.terms.post.stem.n.log                                  50.17209
## prdl.my.fctriPadAir:.clusterid.fctr2                     49.80426
## color.fctrSpace Gray                                     49.63204
## prdl.my.fctriPad2                                        48.61429
## prdl.my.fctriPadmini                                     48.49638
## startprice.dcm1.is9                                      48.37251
## sprice.log10                                             47.59704
## prdl.my.fctriPadmini2:.clusterid.fctr2                   47.13793
## prdl.my.fctriPad1                                        46.63375
## condition.fctrFor parts or not working                   46.18220
## prdl.my.fctriPad1:.clusterid.fctr2                       46.14804
## prdl.my.fctrUnknown:.clusterid.fctr3                     45.97618
## cellular.fctrUnknown:carrier.fctrUnknown                 45.72360
## cellular.fctrUnknown                                     45.68888
## D.terms.post.stop.n.log                                  44.49862
## condition.fctrSeller refurbished                         43.60662
## prdl.my.fctriPad1:.clusterid.fctr3                       42.66158
## condition.fctrManufacturer refurbished                   42.10131
## prdl.my.fctriPad4:.clusterid.fctr2                       39.81232
## sprice.d20nexp                                           38.83227
## D.weight.sum.stem.stop.Ratio                             26.99368
## D.ratio.wrds.stop.n.wrds.n                               26.94246
## prdl.my.fctriPad4:.clusterid.fctr3                        0.00000
# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
    if (!is.null(featsimp_df <- glb_featsimp_df)) {
        featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))    
        featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
        featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)    
        featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat, 
                                            NA, featsimp_df$feat.interact)
        featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
        featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact) 
        featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact, 
                                                            data=featsimp_df, FUN=max))    
        #rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])    
        
        featsimp_df <- subset(featsimp_df, !is.na(importance.max))
        if (nrow(featsimp_df) > 5) {
            warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
            featsimp_df <- head(featsimp_df, 5)
        }
        
    #     if (!all(is.na(featsimp_df$feat.interact)))
    #         stop("not implemented yet")
        rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
        for (var in featsimp_df$feat) {
            plot_df <- melt(obs_df, id.vars=var, 
                            measure.vars=c(glb_rsp_var, rsp_var_out))
    
    #         if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value", 
    #                                              facet_colcol_name="variable") + 
    #                       geom_vline(xintercept=<divider_val>, linetype="dotted")) else     
                print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
                                     facet_colcol_name="variable", jitter=TRUE) + 
                          guides(color=FALSE))
        }
    }
    
    if (glb_is_regression) {
        if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
            warning("No important features in glb_fin_mdl") else
            print(myplot_prediction_regression(df=obs_df, 
                        feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
                                      ".rownames"), 
                                               feat_y=featsimp_df$feat[1],
                        rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
                        id_vars=glb_id_var)
    #               + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
    #               + geom_point(aes_string(color="<col_name>.fctr")) #  to color the plot
                  )
    }    
    
    if (glb_is_classification) {
        if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
            warning("No features in selected model are statistically important")
        else print(myplot_prediction_classification(df=obs_df, 
                feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2], 
                              ".rownames"),
                                               feat_y=featsimp_df$feat[1],
                     rsp_var=glb_rsp_var, 
                     rsp_var_out=rsp_var_out, 
                     id_vars=glb_id_var,
                    prob_threshold=prob_threshold)
#               + geom_hline(yintercept=<divider_val>, linetype = "dotted")
                )
    }    
}

if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df = glb_OOBobs_df, mdl_id = glb_sel_mdl_id, 
            prob_threshold = glb_models_df[glb_models_df$id == glb_sel_mdl_id, 
                                           "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id)                  
## Warning in glb_analytics_diag_plots(obs_df = glb_OOBobs_df, mdl_id =
## glb_sel_mdl_id, : Limiting important feature scatter plots to 5 out of 32

## [1] "Min/Max Boundaries: "
## [1] UniqueID                               
## [2] sold.fctr                              
## [3] sold.fctr.predict.All.X.glmnet.prob    
## [4] sold.fctr.predict.All.X.glmnet         
## [5] sold.fctr.predict.All.X.glmnet.err     
## [6] sold.fctr.predict.All.X.glmnet.err.abs 
## [7] sold.fctr.predict.All.X.glmnet.accurate
## [8] sold.fctr.predict.All.X.glmnet.error   
## [9] .label                                 
## <0 rows> (or 0-length row.names)
## [1] "Inaccurate: "
##      UniqueID sold.fctr sold.fctr.predict.All.X.glmnet.prob
## 1696    11705         Y                         0.003580620
## 1807    11817         Y                         0.008680448
## 1506    11514         Y                         0.022355332
## 958     10961         Y                         0.023805316
## 457     10458         Y                         0.031964256
## 332     10332         Y                         0.037172380
##      sold.fctr.predict.All.X.glmnet sold.fctr.predict.All.X.glmnet.err
## 1696                              N                               TRUE
## 1807                              N                               TRUE
## 1506                              N                               TRUE
## 958                               N                               TRUE
## 457                               N                               TRUE
## 332                               N                               TRUE
##      sold.fctr.predict.All.X.glmnet.err.abs
## 1696                              0.9964194
## 1807                              0.9913196
## 1506                              0.9776447
## 958                               0.9761947
## 457                               0.9680357
## 332                               0.9628276
##      sold.fctr.predict.All.X.glmnet.accurate
## 1696                                   FALSE
## 1807                                   FALSE
## 1506                                   FALSE
## 958                                    FALSE
## 457                                    FALSE
## 332                                    FALSE
##      sold.fctr.predict.All.X.glmnet.error
## 1696                           -0.2964194
## 1807                           -0.2913196
## 1506                           -0.2776447
## 958                            -0.2761947
## 457                            -0.2680357
## 332                            -0.2628276
##      UniqueID sold.fctr sold.fctr.predict.All.X.glmnet.prob
## 1696    11705         Y                          0.00358062
## 45      10045         Y                          0.26431208
## 308     10308         N                          0.34278981
## 838     10841         N                          0.52344726
## 511     10512         N                          0.66487561
## 580     10581         N                          0.89966694
##      sold.fctr.predict.All.X.glmnet sold.fctr.predict.All.X.glmnet.err
## 1696                              N                               TRUE
## 45                                N                               TRUE
## 308                               Y                               TRUE
## 838                               Y                               TRUE
## 511                               Y                               TRUE
## 580                               Y                               TRUE
##      sold.fctr.predict.All.X.glmnet.err.abs
## 1696                              0.9964194
## 45                                0.7356879
## 308                               0.3427898
## 838                               0.5234473
## 511                               0.6648756
## 580                               0.8996669
##      sold.fctr.predict.All.X.glmnet.accurate
## 1696                                   FALSE
## 45                                     FALSE
## 308                                    FALSE
## 838                                    FALSE
## 511                                    FALSE
## 580                                    FALSE
##      sold.fctr.predict.All.X.glmnet.error
## 1696                          -0.29641938
## 45                            -0.03568792
## 308                            0.04278981
## 838                            0.22344726
## 511                            0.36487561
## 580                            0.59966694
##      UniqueID sold.fctr sold.fctr.predict.All.X.glmnet.prob
## 412     10413         N                           0.9743568
## 103     10103         N                           0.9814365
## 199     10199         N                           0.9869369
## 1464    11471         N                           0.9903548
## 490     10491         N                           0.9924276
## 1384    11391         N                           0.9963958
##      sold.fctr.predict.All.X.glmnet sold.fctr.predict.All.X.glmnet.err
## 412                               Y                               TRUE
## 103                               Y                               TRUE
## 199                               Y                               TRUE
## 1464                              Y                               TRUE
## 490                               Y                               TRUE
## 1384                              Y                               TRUE
##      sold.fctr.predict.All.X.glmnet.err.abs
## 412                               0.9743568
## 103                               0.9814365
## 199                               0.9869369
## 1464                              0.9903548
## 490                               0.9924276
## 1384                              0.9963958
##      sold.fctr.predict.All.X.glmnet.accurate
## 412                                    FALSE
## 103                                    FALSE
## 199                                    FALSE
## 1464                                   FALSE
## 490                                    FALSE
## 1384                                   FALSE
##      sold.fctr.predict.All.X.glmnet.error
## 412                             0.6743568
## 103                             0.6814365
## 199                             0.6869369
## 1464                            0.6903548
## 490                             0.6924276
## 1384                            0.6963958

glb_ctgry_df <- merge(glb_ctgry_df, 
        myget_category_stats(obs_df = glb_fitobs_df, mdl_id = glb_sel_mdl_id, label = "fit"),
                      by = glb_category_var, all = TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
glb_ctgry_df <- merge(glb_ctgry_df, 
            myget_category_stats(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id, label="OOB"),
                      #by=glb_category_var, all=TRUE) glb_ctgry-df already contains .n.OOB ?
                      all=TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
if (any(grepl("OOB", glbMdlMetricsEval)))
    print(orderBy(~-err.abs.OOB.mean, glb_ctgry_df)) else
        print(orderBy(~-err.abs.fit.mean, glb_ctgry_df))
##           prdl.my.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## iPad1            iPad1     95    130     88     0.12745098     0.11445783
## iPadmini2    iPadmini2     53     54     56     0.05294118     0.06385542
## Unknown        Unknown     96    108     92     0.10588235     0.11566265
## iPadmini      iPadmini    123    154    111     0.15098039     0.14819277
## iPadAir2      iPadAir2     69    102     62     0.10000000     0.08313253
## iPad2            iPad2    142    144    154     0.14117647     0.17108434
## iPad3            iPad3     61     92     55     0.09019608     0.07349398
## iPadmini3    iPadmini3     36     54     38     0.05294118     0.04337349
## iPadAir        iPadAir     82     98     74     0.09607843     0.09879518
## iPad4            iPad4     73     84     68     0.08235294     0.08795181
##           .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit
## iPad1         0.11027569        23.91786        0.1839835    130
## iPadmini2     0.07017544        14.19476        0.2628659     54
## Unknown       0.11528822        26.52091        0.2455640    108
## iPadmini      0.13909774        30.21777        0.1962193    154
## iPadAir2      0.07769424        21.24831        0.2083168    102
## iPad2         0.19298246        31.86069        0.2212548    144
## iPad3         0.06892231        21.85916        0.2375996     92
## iPadmini3     0.04761905        10.64581        0.1971447     54
## iPadAir       0.09273183        13.80524        0.1408698     98
## iPad4         0.08521303        10.53643        0.1254337     84
##           err.abs.OOB.sum err.abs.OOB.mean
## iPad1           31.738295        0.3340873
## iPadmini2       15.149743        0.2858442
## Unknown         25.545260        0.2660965
## iPadmini        32.704594        0.2658910
## iPadAir2        17.785002        0.2577536
## iPad2           35.143654        0.2474905
## iPad3           14.689955        0.2408189
## iPadmini3        8.566058        0.2379461
## iPadAir         15.249371        0.1859679
## iPad4           13.177556        0.1805145
print(colSums(glb_ctgry_df[, -grep(glb_category_var, names(glb_ctgry_df))]))
##           .n.OOB           .n.Fit           .n.Tst   .freqRatio.Fit 
##       830.000000      1020.000000       798.000000         1.000000 
##   .freqRatio.OOB   .freqRatio.Tst  err.abs.fit.sum err.abs.fit.mean 
##         1.000000         1.000000       204.806955         2.019252 
##           .n.fit  err.abs.OOB.sum err.abs.OOB.mean 
##      1020.000000       209.749487         2.502411
write.csv(glb_OOBobs_df[, c(glb_id_var, 
                grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))], 
    paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE), 
           "_OOBobs.csv"), row.names=FALSE)

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)
##         label step_major step_minor label_minor     bgn     end elapsed
## 12 fit.models          6          2           2 477.215 498.302  21.087
## 13 fit.models          6          3           3 498.303      NA      NA
# if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
#         stop("fit.models_3: Why is this happening ?")

#stop(here"); glb_to_sav()
sync_glb_obs_df <- function() {
    # Merge or cbind ?
    for (col in setdiff(names(glb_fitobs_df), names(glb_trnobs_df)))
        glb_trnobs_df[glb_trnobs_df$.lcn == "Fit", col] <<- glb_fitobs_df[, col]
    for (col in setdiff(names(glb_fitobs_df), names(glb_allobs_df)))
        glb_allobs_df[glb_allobs_df$.lcn == "Fit", col] <<- glb_fitobs_df[, col]
    if (all(is.na(glb_newobs_df[, glb_rsp_var])))
        for (col in setdiff(names(glb_OOBobs_df), names(glb_trnobs_df)))
            glb_trnobs_df[glb_trnobs_df$.lcn == "OOB", col] <<- glb_OOBobs_df[, col]
    for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
        glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <<- glb_OOBobs_df[, col]
}
sync_glb_obs_df()

print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))
## character(0)
if (glb_save_envir)
    save(glb_feats_df, 
         glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
         glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
         glb_model_type,
        file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))

rm(ret_lst)
## Warning in rm(ret_lst): object 'ret_lst' not found
replay.petrisim(pn=glb_analytics_pn, 
    replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "model.selected")), flip_coord=TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0 
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction   firing:  model.selected 
## 3.0000    3   0 2 1 0

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)
##                label step_major step_minor label_minor     bgn     end
## 13        fit.models          6          3           3 498.303 504.741
## 14 fit.data.training          7          0           0 504.742      NA
##    elapsed
## 13   6.438
## 14      NA

Step 7.0: fit data training

#load(paste0(glb_inp_pfx, "dsk.RData"))

#stop(here"); glb_to_sav()
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
    warning("Final model same as user selected model")
    glb_fin_mdl <- glb_models_lst[[glb_fin_mdl_id]]
} else 
# if (nrow(glb_fitobs_df) + length(glbObsFitOutliers) == nrow(glb_trnobs_df))
if (!all(is.na(glb_newobs_df[, glb_rsp_var])))
{    
    warning("Final model same as glb_sel_mdl_id")
    glb_fin_mdl_id <- paste0("Final.", glb_sel_mdl_id)
    glb_fin_mdl <- glb_sel_mdl
    glb_models_lst[[glb_fin_mdl_id]] <- glb_fin_mdl
} else {    

    if (grepl("RFE", glb_sel_mdl_id) || 
        (!is.null(glb_mdl_ensemble) && grepl("RFE", glb_mdl_ensemble))) {
        indep_vars <- myadjust_interaction_feats(subset(glb_feats_df, 
                                            !nzv & (exclude.as.feat != 1))[, "id"])
        rfe_trn_results <- myrun_rfe(glb_trnobs_df, indep_vars, glb_rfe_fit_sizes)
        if (!isTRUE(all.equal(sort(predictors(rfe_trn_results)),
                              sort(predictors(rfe_fit_results))))) {
            print("Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):")
            print(setdiff(predictors(rfe_trn_results), predictors(rfe_fit_results)))
            print("Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):")
            print(setdiff(predictors(rfe_fit_results), predictors(rfe_trn_results)))
        }
    }    

    if (grepl("Ensemble", glb_sel_mdl_id)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), importance > 5)
        # Fit selected models on glb_trnobs_df
        for (mdl_id in gsub(".prob", "", 
                    gsub(glb_rsp_var_out, "", row.names(mdlimp_df), fixed = TRUE),
                            fixed = TRUE)) {
            mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
            mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"), 
                               collapse = ".")
            if (grepl("RFE\\.X\\.", mdlIdPfx)) 
                mdlIndepVars <- myadjust_interaction_feats(myextract_actual_feats(
                    predictors(rfe_trn_results))) else
                mdlIndepVars <- trim(unlist(
            strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
            ret_lst <- 
                myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = mdlIdPfx, 
                        type = glb_model_type, tune.df = glb_tune_models_df,
                        trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds,
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = tail(mdl_id_components, 1))),
                    indep_vars = mdlIndepVars,
                    rsp_var = glb_rsp_var, 
                    fit_df = glb_trnobs_df, OOB_df = NULL)
            
            glb_trnobs_df <- glb_get_predictions(df = glb_trnobs_df,
                                                mdl_id = tail(glb_models_df$id, 1), 
                                                rsp_var_out = glb_rsp_var_out,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
            glb_newobs_df <- glb_get_predictions(df = glb_newobs_df,
                                                mdl_id = tail(glb_models_df$id, 1), 
                                                rsp_var_out = glb_rsp_var_out,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
        }    
    }
    
    print("***************")
    print("Outliers in obsTrnDF not deleted yet")
    print("***************")

    # "Final" model
    if ((model_method <- glb_sel_mdl$method) == "custom")
        # get actual method from the mdl_id
        model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
    
    if (grepl("Ensemble", glb_sel_mdl_id)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), importance > 5)
        if (glb_is_classification && glb_is_binomial)
            indep_vars_vctr <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
                                    row.names(mdlimp_df)) else
            indep_vars_vctr <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
                                    row.names(mdlimp_df))
    } else indep_vars_vctr <- 
                trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
                                                   glb_sel_mdl_id
                                                   , "feats"], "[,]")))
        
    # Discontinuing use of tune_finmdl_df; 
    #   since final model needs to be cved on glb_trnobs_df
#     tune_finmdl_df <- NULL
#     if (nrow(glb_sel_mdl$bestTune) > 0) {
#         for (param in names(glb_sel_mdl$bestTune)) {
#             #print(sprintf("param: %s", param))
#             if (glb_sel_mdl$bestTune[1, param] != "none")
#                 tune_finmdl_df <- rbind(tune_finmdl_df, 
#                     data.frame(parameter=param, 
#                                min=glb_sel_mdl$bestTune[1, param], 
#                                max=glb_sel_mdl$bestTune[1, param], 
#                                by=1)) # by val does not matter
#         }
#     } 
    
    # Sync with parameters in mydsutils.R
#stop(here"); glb_to_sav(); glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
    if (!is.null(glb_preproc_methods) &&
        ((match_pos <- regexpr(gsub(".", "\\.", 
                                    paste(glb_preproc_methods, collapse = "|"),
                                   fixed = TRUE), glb_sel_mdl_id)) != -1))
        ths_preProcess <- str_sub(glb_sel_mdl_id, match_pos, 
                                match_pos + attr(match_pos, "match.length") - 1) else
        ths_preProcess <- NULL                                      

    mdl_id_pfx <- ifelse(grepl("Ensemble", glb_sel_mdl_id),
                                   "Final.Ensemble", "Final")
    trnobs_df <- if (is.null(glbObsTrnOutliers[[mdl_id_pfx]])) glb_trnobs_df else 
        glb_trnobs_df[!(glb_trnobs_df[, glb_id_var] %in%
                            glbObsTrnOutliers[[mdl_id_pfx]]), ]
        
    # Force fitting of Final.glm to identify outliers
    method_vctr <- unique(c("glm", tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)))
    for (method in method_vctr) {
        #source("caret_nominalTrainWorkflow.R")
        
        # glmnet requires at least 2 indep vars
        if ((length(indep_vars_vctr) == 1) && (method %in% "glmnet"))
            next
        
        ret_lst <- 
            myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                id.prefix = mdl_id_pfx, 
                type = glb_model_type, trainControl.method = "repeatedcv",
                trainControl.number = glb_rcv_n_folds, 
                trainControl.repeats = glb_rcv_n_repeats,
                trainControl.classProbs = glb_is_classification,
                trainControl.summaryFunction = glbMdlMetricSummaryFn,
                train.metric = glbMdlMetricSummary, 
                train.maximize = glbMdlMetricMaximize,    
                train.method = method,
                train.preProcess = ths_preProcess)),
                indep_vars = indep_vars_vctr, rsp_var = glb_rsp_var, 
                fit_df = trnobs_df, OOB_df = NULL)
    }
        
    if ((length(method_vctr) == 1) || (method != "glm")) {
        glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]] 
        glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "id"]
    }
}
## [1] "***************"
## [1] "Outliers in obsTrnDF not deleted yet"
## [1] "***************"
## [1] "fitting model: Final.glm"
## [1] "    indep_vars: biddable,sprice.d20nexp,spdiff.cut.fctr,D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,startprice.dcm1.is9,storage.fctr,D.chrs.pnct11.n.log,D.chrs.pnct13.n.log,startprice.dgt2.is9,color.fctr,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,prdl.my.fctr,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.log10,sprice.root2,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## + Fold1.Rep1: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold1.Rep1: parameter=none 
## + Fold2.Rep1: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold2.Rep1: parameter=none 
## + Fold3.Rep1: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3.Rep1: parameter=none 
## + Fold1.Rep2: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold1.Rep2: parameter=none 
## + Fold2.Rep2: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold2.Rep2: parameter=none 
## + Fold3.Rep2: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3.Rep2: parameter=none 
## + Fold1.Rep3: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold1.Rep3: parameter=none 
## + Fold2.Rep3: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold2.Rep3: parameter=none 
## + Fold3.Rep3: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3.Rep3: parameter=none 
## Aggregating results
## Fitting final model on full training set

## 
## Call:
## NULL
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.9681  -0.5779  -0.1363   0.4125   3.2961  
## 
## Coefficients: (26 not defined because of singularities)
##                                               Estimate Std. Error z value
## (Intercept)                                  4.818e+00  1.980e+01   0.243
## .rnorm                                      -2.241e-02  7.036e-02  -0.319
## D.chrs.n.log                                -1.851e+00  1.687e+00  -1.097
## D.chrs.pnct11.n.log                         -1.113e-01  2.756e-01  -0.404
## D.chrs.pnct13.n.log                         -2.204e-01  2.538e-01  -0.868
## D.chrs.uppr.n.log                            2.824e-01  1.511e+00   0.187
## D.ratio.weight.sum.wrds.n                   -6.955e-03  2.420e-01  -0.029
## D.ratio.wrds.stop.n.wrds.n                  -6.235e+00  2.607e+00  -2.392
## D.terms.post.stem.n.log                      1.221e+01  6.344e+00   1.925
## D.terms.post.stop.n.log                     -1.692e+01  6.386e+00  -2.649
## D.weight.post.stem.sum                      -1.434e+00  3.556e+00  -0.403
## D.weight.post.stop.sum                       1.288e+00  3.448e+00   0.374
## D.weight.sum                                        NA         NA      NA
## D.weight.sum.stem.stop.Ratio                 2.281e+00  1.993e+01   0.114
## D.wrds.n.log                                 5.303e+00  1.614e+00   3.285
## D.wrds.stop.n.log                            2.504e-01  4.553e-01   0.550
## D.wrds.unq.n.log                                    NA         NA      NA
## biddable                                     2.218e+00  1.932e-01  11.478
## cellular.fctr1                               1.632e-01  2.387e-01   0.684
## cellular.fctrUnknown                        -5.645e-01  3.629e-01  -1.555
## color.fctrGold                              -2.126e-01  4.745e-01  -0.448
## `color.fctrSpace Gray`                      -9.572e-03  2.867e-01  -0.033
## color.fctrUnknown                           -7.209e-02  1.980e-01  -0.364
## color.fctrWhite                             -1.971e-01  2.186e-01  -0.901
## `condition.fctrFor parts or not working`    -6.153e-01  2.959e-01  -2.079
## `condition.fctrManufacturer refurbished`    -2.487e-01  5.034e-01  -0.494
## condition.fctrNew                            1.134e-01  2.671e-01   0.425
## `condition.fctrNew other (see details)`      6.755e-01  3.804e-01   1.776
## `condition.fctrSeller refurbished`          -7.642e-01  3.297e-01  -2.318
## prdl.my.fctriPad1                           -8.169e-01  4.361e-01  -1.873
## prdl.my.fctriPad2                           -4.572e-01  3.849e-01  -1.188
## prdl.my.fctriPad3                            1.912e-01  4.248e-01   0.450
## prdl.my.fctriPad4                            8.521e-01  4.467e-01   1.908
## prdl.my.fctriPadAir                          1.231e+00  4.419e-01   2.786
## prdl.my.fctriPadAir2                         1.508e+00  4.973e-01   3.032
## prdl.my.fctriPadmini                        -3.018e-01  3.801e-01  -0.794
## prdl.my.fctriPadmini2                        5.812e-01  4.593e-01   1.265
## prdl.my.fctriPadmini3                        4.597e-01  5.288e-01   0.869
## `spdiff.cut.fctr(-100,-10]`                  2.286e+00  2.845e-01   8.035
## `spdiff.cut.fctr(-10,-1]`                    4.108e+00  3.673e-01  11.184
## `spdiff.cut.fctr(-1,0]`                      5.699e+00  7.320e-01   7.786
## `spdiff.cut.fctr(0,1]`                       4.867e+00  8.097e-01   6.012
## `spdiff.cut.fctr(1,10]`                      3.920e+00  3.762e-01  10.419
## `spdiff.cut.fctr(10,100]`                    3.253e+00  3.340e-01   9.739
## `spdiff.cut.fctr(100,1e+03]`                 1.174e+00  5.228e-01   2.246
## sprice.d20nexp                              -1.820e+00  1.815e+00  -1.002
## sprice.log10                                -5.453e-01  5.075e-01  -1.074
## sprice.root2                                -8.693e-02  7.844e-02  -1.108
## startprice.dcm1.is9                         -6.420e-01  2.678e-01  -2.397
## startprice.dcm2.is9                          1.896e-01  2.660e-01   0.713
## startprice.dgt1.is9                          7.679e-02  1.880e-01   0.409
## startprice.dgt2.is9                          6.693e-02  2.192e-01   0.305
## storage.fctr16                              -7.430e-01  4.403e-01  -1.687
## storage.fctr32                              -7.296e-01  4.546e-01  -1.605
## storage.fctr64                              -1.910e-01  4.343e-01  -0.440
## storage.fctrUnknown                         -9.796e-02  5.593e-01  -0.175
## `cellular.fctr0:carrier.fctrNone`                   NA         NA      NA
## `cellular.fctr1:carrier.fctrNone`                   NA         NA      NA
## `cellular.fctrUnknown:carrier.fctrNone`             NA         NA      NA
## `cellular.fctr0:carrier.fctrOther`                  NA         NA      NA
## `cellular.fctr1:carrier.fctrOther`           1.364e+01  1.691e+03   0.008
## `cellular.fctrUnknown:carrier.fctrOther`            NA         NA      NA
## `cellular.fctr0:carrier.fctrSprint`                 NA         NA      NA
## `cellular.fctr1:carrier.fctrSprint`          4.476e-01  5.582e-01   0.802
## `cellular.fctrUnknown:carrier.fctrSprint`           NA         NA      NA
## `cellular.fctr0:carrier.fctrT-Mobile`               NA         NA      NA
## `cellular.fctr1:carrier.fctrT-Mobile`        2.231e-01  6.784e-01   0.329
## `cellular.fctrUnknown:carrier.fctrT-Mobile`         NA         NA      NA
## `cellular.fctr0:carrier.fctrUnknown`                NA         NA      NA
## `cellular.fctr1:carrier.fctrUnknown`        -3.460e-02  4.030e-01  -0.086
## `cellular.fctrUnknown:carrier.fctrUnknown`          NA         NA      NA
## `cellular.fctr0:carrier.fctrVerizon`                NA         NA      NA
## `cellular.fctr1:carrier.fctrVerizon`         1.447e-01  3.391e-01   0.427
## `cellular.fctrUnknown:carrier.fctrVerizon`          NA         NA      NA
## `prdl.my.fctrUnknown:.clusterid.fctr2`       8.204e-01  6.142e-01   1.336
## `prdl.my.fctriPad1:.clusterid.fctr2`        -1.065e+00  5.999e-01  -1.776
## `prdl.my.fctriPad2:.clusterid.fctr2`                NA         NA      NA
## `prdl.my.fctriPad3:.clusterid.fctr2`        -8.253e-01  7.431e-01  -1.111
## `prdl.my.fctriPad4:.clusterid.fctr2`        -2.964e-01  7.953e-01  -0.373
## `prdl.my.fctriPadAir:.clusterid.fctr2`       4.084e-01  5.991e-01   0.682
## `prdl.my.fctriPadAir2:.clusterid.fctr2`             NA         NA      NA
## `prdl.my.fctriPadmini:.clusterid.fctr2`             NA         NA      NA
## `prdl.my.fctriPadmini2:.clusterid.fctr2`    -6.078e-01  8.755e-01  -0.694
## `prdl.my.fctriPadmini3:.clusterid.fctr2`            NA         NA      NA
## `prdl.my.fctrUnknown:.clusterid.fctr3`       6.544e-02  8.082e-01   0.081
## `prdl.my.fctriPad1:.clusterid.fctr3`        -1.499e+00  7.064e-01  -2.121
## `prdl.my.fctriPad2:.clusterid.fctr3`                NA         NA      NA
## `prdl.my.fctriPad3:.clusterid.fctr3`                NA         NA      NA
## `prdl.my.fctriPad4:.clusterid.fctr3`        -1.618e+01  3.993e+02  -0.041
## `prdl.my.fctriPadAir:.clusterid.fctr3`              NA         NA      NA
## `prdl.my.fctriPadAir2:.clusterid.fctr3`             NA         NA      NA
## `prdl.my.fctriPadmini:.clusterid.fctr3`             NA         NA      NA
## `prdl.my.fctriPadmini2:.clusterid.fctr3`            NA         NA      NA
## `prdl.my.fctriPadmini3:.clusterid.fctr3`            NA         NA      NA
##                                             Pr(>|z|)    
## (Intercept)                                  0.80769    
## .rnorm                                       0.75004    
## D.chrs.n.log                                 0.27271    
## D.chrs.pnct11.n.log                          0.68629    
## D.chrs.pnct13.n.log                          0.38525    
## D.chrs.uppr.n.log                            0.85178    
## D.ratio.weight.sum.wrds.n                    0.97708    
## D.ratio.wrds.stop.n.wrds.n                   0.01676 *  
## D.terms.post.stem.n.log                      0.05428 .  
## D.terms.post.stop.n.log                      0.00807 ** 
## D.weight.post.stem.sum                       0.68672    
## D.weight.post.stop.sum                       0.70870    
## D.weight.sum                                      NA    
## D.weight.sum.stem.stop.Ratio                 0.90889    
## D.wrds.n.log                                 0.00102 ** 
## D.wrds.stop.n.log                            0.58241    
## D.wrds.unq.n.log                                  NA    
## biddable                                     < 2e-16 ***
## cellular.fctr1                               0.49420    
## cellular.fctrUnknown                         0.11987    
## color.fctrGold                               0.65405    
## `color.fctrSpace Gray`                       0.97336    
## color.fctrUnknown                            0.71579    
## color.fctrWhite                              0.36733    
## `condition.fctrFor parts or not working`     0.03760 *  
## `condition.fctrManufacturer refurbished`     0.62137    
## condition.fctrNew                            0.67100    
## `condition.fctrNew other (see details)`      0.07575 .  
## `condition.fctrSeller refurbished`           0.02045 *  
## prdl.my.fctriPad1                            0.06103 .  
## prdl.my.fctriPad2                            0.23493    
## prdl.my.fctriPad3                            0.65259    
## prdl.my.fctriPad4                            0.05642 .  
## prdl.my.fctriPadAir                          0.00533 ** 
## prdl.my.fctriPadAir2                         0.00243 ** 
## prdl.my.fctriPadmini                         0.42718    
## prdl.my.fctriPadmini2                        0.20571    
## prdl.my.fctriPadmini3                        0.38471    
## `spdiff.cut.fctr(-100,-10]`                 9.38e-16 ***
## `spdiff.cut.fctr(-10,-1]`                    < 2e-16 ***
## `spdiff.cut.fctr(-1,0]`                     6.93e-15 ***
## `spdiff.cut.fctr(0,1]`                      1.84e-09 ***
## `spdiff.cut.fctr(1,10]`                      < 2e-16 ***
## `spdiff.cut.fctr(10,100]`                    < 2e-16 ***
## `spdiff.cut.fctr(100,1e+03]`                 0.02472 *  
## sprice.d20nexp                               0.31610    
## sprice.log10                                 0.28263    
## sprice.root2                                 0.26778    
## startprice.dcm1.is9                          0.01653 *  
## startprice.dcm2.is9                          0.47601    
## startprice.dgt1.is9                          0.68290    
## startprice.dgt2.is9                          0.76007    
## storage.fctr16                               0.09154 .  
## storage.fctr32                               0.10845    
## storage.fctr64                               0.66013    
## storage.fctrUnknown                          0.86096    
## `cellular.fctr0:carrier.fctrNone`                 NA    
## `cellular.fctr1:carrier.fctrNone`                 NA    
## `cellular.fctrUnknown:carrier.fctrNone`           NA    
## `cellular.fctr0:carrier.fctrOther`                NA    
## `cellular.fctr1:carrier.fctrOther`           0.99357    
## `cellular.fctrUnknown:carrier.fctrOther`          NA    
## `cellular.fctr0:carrier.fctrSprint`               NA    
## `cellular.fctr1:carrier.fctrSprint`          0.42261    
## `cellular.fctrUnknown:carrier.fctrSprint`         NA    
## `cellular.fctr0:carrier.fctrT-Mobile`             NA    
## `cellular.fctr1:carrier.fctrT-Mobile`        0.74229    
## `cellular.fctrUnknown:carrier.fctrT-Mobile`       NA    
## `cellular.fctr0:carrier.fctrUnknown`              NA    
## `cellular.fctr1:carrier.fctrUnknown`         0.93159    
## `cellular.fctrUnknown:carrier.fctrUnknown`        NA    
## `cellular.fctr0:carrier.fctrVerizon`              NA    
## `cellular.fctr1:carrier.fctrVerizon`         0.66957    
## `cellular.fctrUnknown:carrier.fctrVerizon`        NA    
## `prdl.my.fctrUnknown:.clusterid.fctr2`       0.18165    
## `prdl.my.fctriPad1:.clusterid.fctr2`         0.07578 .  
## `prdl.my.fctriPad2:.clusterid.fctr2`              NA    
## `prdl.my.fctriPad3:.clusterid.fctr2`         0.26669    
## `prdl.my.fctriPad4:.clusterid.fctr2`         0.70943    
## `prdl.my.fctriPadAir:.clusterid.fctr2`       0.49539    
## `prdl.my.fctriPadAir2:.clusterid.fctr2`           NA    
## `prdl.my.fctriPadmini:.clusterid.fctr2`           NA    
## `prdl.my.fctriPadmini2:.clusterid.fctr2`     0.48751    
## `prdl.my.fctriPadmini3:.clusterid.fctr2`          NA    
## `prdl.my.fctrUnknown:.clusterid.fctr3`       0.93547    
## `prdl.my.fctriPad1:.clusterid.fctr3`         0.03389 *  
## `prdl.my.fctriPad2:.clusterid.fctr3`              NA    
## `prdl.my.fctriPad3:.clusterid.fctr3`              NA    
## `prdl.my.fctriPad4:.clusterid.fctr3`         0.96768    
## `prdl.my.fctriPadAir:.clusterid.fctr3`            NA    
## `prdl.my.fctriPadAir2:.clusterid.fctr3`           NA    
## `prdl.my.fctriPadmini:.clusterid.fctr3`           NA    
## `prdl.my.fctriPadmini2:.clusterid.fctr3`          NA    
## `prdl.my.fctriPadmini3:.clusterid.fctr3`          NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2554.0  on 1849  degrees of freedom
## Residual deviance: 1329.2  on 1782  degrees of freedom
## AIC: 1465.2
## 
## Number of Fisher Scoring iterations: 15
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

##   sold.fctr sold.fctr.predict.Final.glm.N sold.fctr.predict.Final.glm.Y
## 1         N                           833                           162
## 2         Y                           138                           717
##          Prediction
## Reference   N   Y
##         N 833 162
##         Y 138 717
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   8.378378e-01   6.744505e-01   8.202383e-01   8.543588e-01   5.378378e-01 
## AccuracyPValue  McnemarPValue 
##  3.770097e-164   1.842093e-01 
##          id
## 1 Final.glm
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    feats
## 1 biddable,sprice.d20nexp,spdiff.cut.fctr,D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,startprice.dcm1.is9,storage.fctr,D.chrs.pnct11.n.log,D.chrs.pnct13.n.log,startprice.dgt2.is9,color.fctr,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,prdl.my.fctr,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.log10,sprice.root2,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1               1                      4.081                 0.326
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.8376238    0.8904523    0.7847953       0.9180828
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.4       0.8269896        0.8185584
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.8202383             0.8543588     0.6330313
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01101111       0.0227628
## [1] "fitting model: Final.glmnet"
## [1] "    indep_vars: biddable,sprice.d20nexp,spdiff.cut.fctr,D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,startprice.dcm1.is9,storage.fctr,D.chrs.pnct11.n.log,D.chrs.pnct13.n.log,startprice.dgt2.is9,color.fctr,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,prdl.my.fctr,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.log10,sprice.root2,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.00548 on full training set
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha

##             Length Class      Mode     
## a0            85   -none-     numeric  
## beta        7905   dgCMatrix  S4       
## df            85   -none-     numeric  
## dim            2   -none-     numeric  
## lambda        85   -none-     numeric  
## dev.ratio     85   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## classnames     2   -none-     character
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        93   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      2   -none-     character
## [1] "min lambda > lambdaOpt:"
##                              (Intercept) 
##                            -0.5748424332 
##                      D.chrs.pnct13.n.log 
##                            -0.0154166095 
##                D.ratio.weight.sum.wrds.n 
##                            -0.0004393607 
##                  D.terms.post.stop.n.log 
##                            -0.0089438359 
##                                 biddable 
##                             1.9394377386 
##                           cellular.fctr1 
##                             0.0702567191 
##                     cellular.fctrUnknown 
##                            -0.1320135490 
##                          color.fctrWhite 
##                            -0.0266151806 
##   condition.fctrFor parts or not working 
##                            -0.2200229713 
##    condition.fctrNew other (see details) 
##                             0.4387934720 
##         condition.fctrSeller refurbished 
##                            -0.4201823354 
##                        prdl.my.fctriPad1 
##                            -0.4548555560 
##                        prdl.my.fctriPad2 
##                            -0.3245612053 
##                        prdl.my.fctriPad4 
##                             0.1977145978 
##                      prdl.my.fctriPadAir 
##                             0.6128927100 
##                     prdl.my.fctriPadAir2 
##                             0.8968352444 
##                     prdl.my.fctriPadmini 
##                            -0.1622395269 
##                    prdl.my.fctriPadmini2 
##                             0.1674866134 
##                spdiff.cut.fctr(-100,-10] 
##                             1.1267058623 
##                  spdiff.cut.fctr(-10,-1] 
##                             2.6792229681 
##                    spdiff.cut.fctr(-1,0] 
##                             3.6061648196 
##                     spdiff.cut.fctr(0,1] 
##                             2.7445701555 
##                    spdiff.cut.fctr(1,10] 
##                             2.4590410900 
##                  spdiff.cut.fctr(10,100] 
##                             2.0111381095 
##               spdiff.cut.fctr(100,1e+03] 
##                             0.2412671988 
##                             sprice.log10 
##                            -0.1516075270 
##                             sprice.root2 
##                            -0.0864776262 
##                      startprice.dcm1.is9 
##                            -0.2865501177 
##                           storage.fctr32 
##                            -0.0032900963 
##                           storage.fctr64 
##                             0.2388740478 
##        cellular.fctr1:carrier.fctrSprint 
##                             0.1795038991 
##      cellular.fctr1:carrier.fctrT-Mobile 
##                             0.0553779569 
## cellular.fctrUnknown:carrier.fctrUnknown 
##                            -0.0076892795 
##     prdl.my.fctrUnknown:.clusterid.fctr2 
##                             0.3891983721 
##       prdl.my.fctriPad1:.clusterid.fctr2 
##                            -0.4118967518 
##       prdl.my.fctriPad3:.clusterid.fctr2 
##                            -0.2066322688 
##       prdl.my.fctriPad1:.clusterid.fctr3 
##                            -0.7279651218 
##       prdl.my.fctriPad4:.clusterid.fctr3 
##                            -2.2439690983 
## [1] "max lambda < lambdaOpt:"
##                              (Intercept) 
##                            -0.4887481635 
##                      D.chrs.pnct11.n.log 
##                            -0.0049897588 
##                      D.chrs.pnct13.n.log 
##                            -0.0343205066 
##                D.ratio.weight.sum.wrds.n 
##                            -0.0065169343 
##                  D.terms.post.stop.n.log 
##                            -0.0039264020 
##             D.weight.sum.stem.stop.Ratio 
##                            -0.1489884401 
##                                 biddable 
##                             1.9631938033 
##                           cellular.fctr1 
##                             0.0786061208 
##                     cellular.fctrUnknown 
##                            -0.1412878868 
##                          color.fctrWhite 
##                            -0.0385898938 
##   condition.fctrFor parts or not working 
##                            -0.2344565867 
##    condition.fctrNew other (see details) 
##                             0.4627975233 
##         condition.fctrSeller refurbished 
##                            -0.4454800069 
##                        prdl.my.fctriPad1 
##                            -0.4810735152 
##                        prdl.my.fctriPad2 
##                            -0.3463142796 
##                        prdl.my.fctriPad4 
##                             0.2270111894 
##                      prdl.my.fctriPadAir 
##                             0.6385121785 
##                     prdl.my.fctriPadAir2 
##                             0.9150943539 
##                     prdl.my.fctriPadmini 
##                            -0.1801302700 
##                    prdl.my.fctriPadmini2 
##                             0.1762468108 
##                spdiff.cut.fctr(-100,-10] 
##                             1.2080758350 
##                  spdiff.cut.fctr(-10,-1] 
##                             2.7783245058 
##                    spdiff.cut.fctr(-1,0] 
##                             3.7489075715 
##                     spdiff.cut.fctr(0,1] 
##                             2.8862595318 
##                    spdiff.cut.fctr(1,10] 
##                             2.5615257475 
##                  spdiff.cut.fctr(10,100] 
##                             2.0983774318 
##               spdiff.cut.fctr(100,1e+03] 
##                             0.3228440594 
##                             sprice.log10 
##                            -0.1541621517 
##                             sprice.root2 
##                            -0.0866668571 
##                      startprice.dcm1.is9 
##                            -0.2961675551 
##                           storage.fctr32 
##                            -0.0092455272 
##                           storage.fctr64 
##                             0.2513803695 
##        cellular.fctr1:carrier.fctrSprint 
##                             0.2045698195 
##      cellular.fctr1:carrier.fctrT-Mobile 
##                             0.0719134607 
## cellular.fctrUnknown:carrier.fctrUnknown 
##                            -0.0119069939 
##     prdl.my.fctrUnknown:.clusterid.fctr2 
##                             0.4302836448 
##       prdl.my.fctriPad1:.clusterid.fctr2 
##                            -0.4315400934 
##       prdl.my.fctriPad3:.clusterid.fctr2 
##                            -0.2494241814 
##     prdl.my.fctrUnknown:.clusterid.fctr3 
##                            -0.0005985027 
##       prdl.my.fctriPad1:.clusterid.fctr3 
##                            -0.7691702044 
##       prdl.my.fctriPad4:.clusterid.fctr3 
##                            -2.4003871462

##   sold.fctr sold.fctr.predict.Final.glmnet.N
## 1         N                              831
## 2         Y                              138
##   sold.fctr.predict.Final.glmnet.Y
## 1                              164
## 2                              717
##          Prediction
## Reference   N   Y
##         N 831 164
##         Y 138 717
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   8.367568e-01   6.723340e-01   8.191140e-01   8.533244e-01   5.378378e-01 
## AccuracyPValue  McnemarPValue 
##  7.369741e-163   1.502672e-01 
##             id
## 1 Final.glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    feats
## 1 biddable,sprice.d20nexp,spdiff.cut.fctr,D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,startprice.dcm1.is9,storage.fctr,D.chrs.pnct11.n.log,D.chrs.pnct13.n.log,startprice.dgt2.is9,color.fctr,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,prdl.my.fctr,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.log10,sprice.root2,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                      17.71                 1.915
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.8279468    0.9015075     0.754386        0.908196
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.4       0.8260369        0.8203656
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1              0.819114             0.8533244     0.6357114
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01119409      0.02374578
rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)
##                label step_major step_minor label_minor     bgn    end
## 14 fit.data.training          7          0           0 504.742 533.21
## 15 fit.data.training          7          1           1 533.211     NA
##    elapsed
## 14  28.468
## 15      NA
#stop(here"); glb_to_sav()
if (glb_is_classification && glb_is_binomial) 
    prob_threshold <- glb_models_df[glb_models_df$id == glb_sel_mdl_id,
                                        "opt.prob.threshold.OOB"] else 
    prob_threshold <- NULL

if (grepl("Ensemble", glb_fin_mdl_id)) {
    # Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
    mdlEnsembleComps <- unlist(str_split(subset(glb_models_df, 
                                                id == glb_fin_mdl_id)$feats, ","))
    if (glb_is_classification && glb_is_binomial)
        mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
    mdlEnsembleComps <- gsub(paste0("^", 
                                    gsub(".", "\\.", glb_rsp_var_out, fixed = TRUE)),
                             "", mdlEnsembleComps)
    for (mdl_id in mdlEnsembleComps) {
        glb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=mdl_id, 
                                            rsp_var_out=glb_rsp_var_out,
                                            prob_threshold_def=prob_threshold)
        glb_newobs_df <- glb_get_predictions(df=glb_newobs_df, mdl_id=mdl_id, 
                                            rsp_var_out=glb_rsp_var_out,
                                            prob_threshold_def=prob_threshold)
    }    
}
glb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id, 
                                     rsp_var_out=glb_rsp_var_out,
                                    prob_threshold_def=prob_threshold)
## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Using default probability threshold: 0.3
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
                                          featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)
##                                           All.X.glmnet.importance
## spdiff.cut.fctr(-1,0]                                   100.00000
## spdiff.cut.fctr(0,1]                                     91.77769
## spdiff.cut.fctr(-10,-1]                                  89.53891
## spdiff.cut.fctr(1,10]                                    91.77825
## spdiff.cut.fctr(10,100]                                  86.86797
## biddable                                                 75.17563
## spdiff.cut.fctr(-100,-10]                                73.61063
## prdl.my.fctriPadAir2                                     69.61045
## prdl.my.fctriPadAir                                      66.43833
## condition.fctrNew other (see details)                    59.12005
## prdl.my.fctrUnknown:.clusterid.fctr2                     60.62908
## spdiff.cut.fctr(100,1e+03]                               62.38931
## storage.fctr64                                           54.64454
## prdl.my.fctriPad4                                        65.16814
## cellular.fctr1:carrier.fctrSprint                        61.81532
## prdl.my.fctriPadmini2                                    64.52272
## cellular.fctr1                                           52.71142
## cellular.fctr1:carrier.fctrT-Mobile                      50.21020
## .rnorm                                                   52.38568
## D.chrs.n.log                                             50.18255
## D.chrs.uppr.n.log                                        52.60858
## D.ratio.wrds.stop.n.wrds.n                               26.94246
## D.terms.post.stem.n.log                                  50.17209
## D.weight.post.stem.sum                                   51.63919
## D.weight.post.stop.sum                                   52.71142
## D.weight.sum                                             51.66462
## D.wrds.n.log                                             63.88632
## D.wrds.stop.n.log                                        53.73265
## D.wrds.unq.n.log                                         51.14189
## cellular.fctr0:carrier.fctrNone                          54.22434
## cellular.fctr0:carrier.fctrOther                         52.71142
## cellular.fctr0:carrier.fctrSprint                        52.71142
## cellular.fctr0:carrier.fctrT-Mobile                      52.71142
## cellular.fctr0:carrier.fctrUnknown                       52.71142
## cellular.fctr0:carrier.fctrVerizon                       52.71142
## cellular.fctr1:carrier.fctrNone                          52.71142
## cellular.fctr1:carrier.fctrOther                         75.82223
## cellular.fctr1:carrier.fctrUnknown                       52.55260
## cellular.fctr1:carrier.fctrVerizon                       55.50911
## cellular.fctrUnknown:carrier.fctrNone                    52.71142
## cellular.fctrUnknown:carrier.fctrOther                   52.71142
## cellular.fctrUnknown:carrier.fctrSprint                  52.71142
## cellular.fctrUnknown:carrier.fctrT-Mobile                52.71142
## cellular.fctrUnknown:carrier.fctrVerizon                 52.71142
## color.fctrGold                                           53.48381
## color.fctrSpace Gray                                     49.63204
## color.fctrUnknown                                        55.86778
## condition.fctrManufacturer refurbished                   42.10131
## condition.fctrNew                                        52.97506
## prdl.my.fctriPad2:.clusterid.fctr2                       52.71142
## prdl.my.fctriPad2:.clusterid.fctr3                       52.71142
## prdl.my.fctriPad3                                        54.34045
## prdl.my.fctriPad3:.clusterid.fctr3                       52.71142
## prdl.my.fctriPad4:.clusterid.fctr2                       39.81232
## prdl.my.fctriPadAir2:.clusterid.fctr2                    52.71142
## prdl.my.fctriPadAir2:.clusterid.fctr3                    52.71142
## prdl.my.fctriPadAir:.clusterid.fctr2                     49.80426
## prdl.my.fctriPadAir:.clusterid.fctr3                     52.71142
## prdl.my.fctriPadmini2:.clusterid.fctr2                   47.13793
## prdl.my.fctriPadmini2:.clusterid.fctr3                   52.71142
## prdl.my.fctriPadmini3                                    59.42417
## prdl.my.fctriPadmini3:.clusterid.fctr2                   52.71142
## prdl.my.fctriPadmini3:.clusterid.fctr3                   52.71142
## prdl.my.fctriPadmini:.clusterid.fctr2                    52.71142
## prdl.my.fctriPadmini:.clusterid.fctr3                    52.71142
## sprice.d20nexp                                           38.83227
## startprice.dcm2.is9                                      51.96783
## startprice.dgt1.is9                                      52.93673
## startprice.dgt2.is9                                      53.73722
## storage.fctr16                                           51.55882
## storage.fctrUnknown                                      68.07849
## prdl.my.fctrUnknown:.clusterid.fctr3                     45.97618
## D.chrs.pnct11.n.log                                      56.89033
## D.ratio.weight.sum.wrds.n                                52.18561
## storage.fctr32                                           52.71638
## D.weight.sum.stem.stop.Ratio                             26.99368
## cellular.fctrUnknown:carrier.fctrUnknown                 45.72360
## D.terms.post.stop.n.log                                  44.49862
## D.chrs.pnct13.n.log                                      51.24550
## color.fctrWhite                                          50.71378
## sprice.root2                                             51.74950
## cellular.fctrUnknown                                     45.68888
## sprice.log10                                             47.59704
## prdl.my.fctriPadmini                                     48.49638
## prdl.my.fctriPad3:.clusterid.fctr2                       55.10589
## condition.fctrFor parts or not working                   46.18220
## startprice.dcm1.is9                                      48.37251
## prdl.my.fctriPad2                                        48.61429
## prdl.my.fctriPad1:.clusterid.fctr2                       46.14804
## condition.fctrSeller refurbished                         43.60662
## prdl.my.fctriPad1                                        46.63375
## prdl.my.fctriPad1:.clusterid.fctr3                       42.66158
## prdl.my.fctriPad4:.clusterid.fctr3                        0.00000
##                                           importance
## spdiff.cut.fctr(-1,0]                      100.00000
## spdiff.cut.fctr(0,1]                        85.31015
## spdiff.cut.fctr(-10,-1]                     84.15852
## spdiff.cut.fctr(1,10]                       80.40773
## spdiff.cut.fctr(10,100]                     72.75819
## biddable                                    71.47982
## spdiff.cut.fctr(-100,-10]                   57.67475
## prdl.my.fctriPadAir2                        53.70014
## prdl.my.fctriPadAir                         48.86583
## condition.fctrNew other (see details)       45.89626
## prdl.my.fctrUnknown:.clusterid.fctr2        45.06580
## spdiff.cut.fctr(100,1e+03]                  42.57950
## storage.fctr64                              42.47779
## prdl.my.fctriPad4                           41.79089
## cellular.fctr1:carrier.fctrSprint           41.47670
## prdl.my.fctriPadmini2                       41.25744
## cellular.fctr1                              39.59945
## cellular.fctr1:carrier.fctrT-Mobile         39.35301
## .rnorm                                      38.39431
## D.chrs.n.log                                38.39431
## D.chrs.uppr.n.log                           38.39431
## D.ratio.wrds.stop.n.wrds.n                  38.39431
## D.terms.post.stem.n.log                     38.39431
## D.weight.post.stem.sum                      38.39431
## D.weight.post.stop.sum                      38.39431
## D.weight.sum                                38.39431
## D.wrds.n.log                                38.39431
## D.wrds.stop.n.log                           38.39431
## D.wrds.unq.n.log                            38.39431
## cellular.fctr0:carrier.fctrNone             38.39431
## cellular.fctr0:carrier.fctrOther            38.39431
## cellular.fctr0:carrier.fctrSprint           38.39431
## cellular.fctr0:carrier.fctrT-Mobile         38.39431
## cellular.fctr0:carrier.fctrUnknown          38.39431
## cellular.fctr0:carrier.fctrVerizon          38.39431
## cellular.fctr1:carrier.fctrNone             38.39431
## cellular.fctr1:carrier.fctrOther            38.39431
## cellular.fctr1:carrier.fctrUnknown          38.39431
## cellular.fctr1:carrier.fctrVerizon          38.39431
## cellular.fctrUnknown:carrier.fctrNone       38.39431
## cellular.fctrUnknown:carrier.fctrOther      38.39431
## cellular.fctrUnknown:carrier.fctrSprint     38.39431
## cellular.fctrUnknown:carrier.fctrT-Mobile   38.39431
## cellular.fctrUnknown:carrier.fctrVerizon    38.39431
## color.fctrGold                              38.39431
## color.fctrSpace Gray                        38.39431
## color.fctrUnknown                           38.39431
## condition.fctrManufacturer refurbished      38.39431
## condition.fctrNew                           38.39431
## prdl.my.fctriPad2:.clusterid.fctr2          38.39431
## prdl.my.fctriPad2:.clusterid.fctr3          38.39431
## prdl.my.fctriPad3                           38.39431
## prdl.my.fctriPad3:.clusterid.fctr3          38.39431
## prdl.my.fctriPad4:.clusterid.fctr2          38.39431
## prdl.my.fctriPadAir2:.clusterid.fctr2       38.39431
## prdl.my.fctriPadAir2:.clusterid.fctr3       38.39431
## prdl.my.fctriPadAir:.clusterid.fctr2        38.39431
## prdl.my.fctriPadAir:.clusterid.fctr3        38.39431
## prdl.my.fctriPadmini2:.clusterid.fctr2      38.39431
## prdl.my.fctriPadmini2:.clusterid.fctr3      38.39431
## prdl.my.fctriPadmini3                       38.39431
## prdl.my.fctriPadmini3:.clusterid.fctr2      38.39431
## prdl.my.fctriPadmini3:.clusterid.fctr3      38.39431
## prdl.my.fctriPadmini:.clusterid.fctr2       38.39431
## prdl.my.fctriPadmini:.clusterid.fctr3       38.39431
## sprice.d20nexp                              38.39431
## startprice.dcm2.is9                         38.39431
## startprice.dgt1.is9                         38.39431
## startprice.dgt2.is9                         38.39431
## storage.fctr16                              38.39431
## storage.fctrUnknown                         38.39431
## prdl.my.fctrUnknown:.clusterid.fctr3        38.39379
## D.chrs.pnct11.n.log                         38.38991
## D.ratio.weight.sum.wrds.n                   38.38146
## storage.fctr32                              38.33297
## D.weight.sum.stem.stop.Ratio                38.26292
## cellular.fctrUnknown:carrier.fctrUnknown    38.25950
## D.terms.post.stop.n.log                     38.24626
## D.chrs.pnct13.n.log                         38.11481
## color.fctrWhite                             37.93000
## sprice.root2                                36.91983
## cellular.fctrUnknown                        36.13550
## sprice.log10                                35.80737
## prdl.my.fctriPadmini                        35.61259
## prdl.my.fctriPad3:.clusterid.fctr2          34.83380
## condition.fctrFor parts or not working      34.63052
## startprice.dcm1.is9                         33.50057
## prdl.my.fctriPad2                           32.84184
## prdl.my.fctriPad1:.clusterid.fctr2          31.35476
## condition.fctrSeller refurbished            31.20851
## prdl.my.fctriPad1                           30.61657
## prdl.my.fctriPad1:.clusterid.fctr3          25.94724
## prdl.my.fctriPad4:.clusterid.fctr3           0.00000
##                                           Final.glmnet.importance
## spdiff.cut.fctr(-1,0]                                   100.00000
## spdiff.cut.fctr(0,1]                                     85.31015
## spdiff.cut.fctr(-10,-1]                                  84.15852
## spdiff.cut.fctr(1,10]                                    80.40773
## spdiff.cut.fctr(10,100]                                  72.75819
## biddable                                                 71.47982
## spdiff.cut.fctr(-100,-10]                                57.67475
## prdl.my.fctriPadAir2                                     53.70014
## prdl.my.fctriPadAir                                      48.86583
## condition.fctrNew other (see details)                    45.89626
## prdl.my.fctrUnknown:.clusterid.fctr2                     45.06580
## spdiff.cut.fctr(100,1e+03]                               42.57950
## storage.fctr64                                           42.47779
## prdl.my.fctriPad4                                        41.79089
## cellular.fctr1:carrier.fctrSprint                        41.47670
## prdl.my.fctriPadmini2                                    41.25744
## cellular.fctr1                                           39.59945
## cellular.fctr1:carrier.fctrT-Mobile                      39.35301
## .rnorm                                                   38.39431
## D.chrs.n.log                                             38.39431
## D.chrs.uppr.n.log                                        38.39431
## D.ratio.wrds.stop.n.wrds.n                               38.39431
## D.terms.post.stem.n.log                                  38.39431
## D.weight.post.stem.sum                                   38.39431
## D.weight.post.stop.sum                                   38.39431
## D.weight.sum                                             38.39431
## D.wrds.n.log                                             38.39431
## D.wrds.stop.n.log                                        38.39431
## D.wrds.unq.n.log                                         38.39431
## cellular.fctr0:carrier.fctrNone                          38.39431
## cellular.fctr0:carrier.fctrOther                         38.39431
## cellular.fctr0:carrier.fctrSprint                        38.39431
## cellular.fctr0:carrier.fctrT-Mobile                      38.39431
## cellular.fctr0:carrier.fctrUnknown                       38.39431
## cellular.fctr0:carrier.fctrVerizon                       38.39431
## cellular.fctr1:carrier.fctrNone                          38.39431
## cellular.fctr1:carrier.fctrOther                         38.39431
## cellular.fctr1:carrier.fctrUnknown                       38.39431
## cellular.fctr1:carrier.fctrVerizon                       38.39431
## cellular.fctrUnknown:carrier.fctrNone                    38.39431
## cellular.fctrUnknown:carrier.fctrOther                   38.39431
## cellular.fctrUnknown:carrier.fctrSprint                  38.39431
## cellular.fctrUnknown:carrier.fctrT-Mobile                38.39431
## cellular.fctrUnknown:carrier.fctrVerizon                 38.39431
## color.fctrGold                                           38.39431
## color.fctrSpace Gray                                     38.39431
## color.fctrUnknown                                        38.39431
## condition.fctrManufacturer refurbished                   38.39431
## condition.fctrNew                                        38.39431
## prdl.my.fctriPad2:.clusterid.fctr2                       38.39431
## prdl.my.fctriPad2:.clusterid.fctr3                       38.39431
## prdl.my.fctriPad3                                        38.39431
## prdl.my.fctriPad3:.clusterid.fctr3                       38.39431
## prdl.my.fctriPad4:.clusterid.fctr2                       38.39431
## prdl.my.fctriPadAir2:.clusterid.fctr2                    38.39431
## prdl.my.fctriPadAir2:.clusterid.fctr3                    38.39431
## prdl.my.fctriPadAir:.clusterid.fctr2                     38.39431
## prdl.my.fctriPadAir:.clusterid.fctr3                     38.39431
## prdl.my.fctriPadmini2:.clusterid.fctr2                   38.39431
## prdl.my.fctriPadmini2:.clusterid.fctr3                   38.39431
## prdl.my.fctriPadmini3                                    38.39431
## prdl.my.fctriPadmini3:.clusterid.fctr2                   38.39431
## prdl.my.fctriPadmini3:.clusterid.fctr3                   38.39431
## prdl.my.fctriPadmini:.clusterid.fctr2                    38.39431
## prdl.my.fctriPadmini:.clusterid.fctr3                    38.39431
## sprice.d20nexp                                           38.39431
## startprice.dcm2.is9                                      38.39431
## startprice.dgt1.is9                                      38.39431
## startprice.dgt2.is9                                      38.39431
## storage.fctr16                                           38.39431
## storage.fctrUnknown                                      38.39431
## prdl.my.fctrUnknown:.clusterid.fctr3                     38.39379
## D.chrs.pnct11.n.log                                      38.38991
## D.ratio.weight.sum.wrds.n                                38.38146
## storage.fctr32                                           38.33297
## D.weight.sum.stem.stop.Ratio                             38.26292
## cellular.fctrUnknown:carrier.fctrUnknown                 38.25950
## D.terms.post.stop.n.log                                  38.24626
## D.chrs.pnct13.n.log                                      38.11481
## color.fctrWhite                                          37.93000
## sprice.root2                                             36.91983
## cellular.fctrUnknown                                     36.13550
## sprice.log10                                             35.80737
## prdl.my.fctriPadmini                                     35.61259
## prdl.my.fctriPad3:.clusterid.fctr2                       34.83380
## condition.fctrFor parts or not working                   34.63052
## startprice.dcm1.is9                                      33.50057
## prdl.my.fctriPad2                                        32.84184
## prdl.my.fctriPad1:.clusterid.fctr2                       31.35476
## condition.fctrSeller refurbished                         31.20851
## prdl.my.fctriPad1                                        30.61657
## prdl.my.fctriPad1:.clusterid.fctr3                       25.94724
## prdl.my.fctriPad4:.clusterid.fctr3                        0.00000
if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id, 
            prob_threshold=glb_models_df[glb_models_df$id == glb_sel_mdl_id, 
                                         "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id)                  
## Warning in glb_analytics_diag_plots(obs_df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 32

## [1] "Min/Max Boundaries: "
##     UniqueID sold.fctr sold.fctr.predict.All.X.glmnet.prob
## 15     10015         Y                           0.2387295
## 27     10027         Y                                  NA
## 2      10002         Y                           0.9976313
## 23     10023         N                                  NA
## 195    10195         N                           0.1991452
## 1      10001         N                           0.2771651
## 22     10022         N                           0.5305515
## 20     10020         N                           0.5492854
## 135    10135         N                           0.6032367
## 127    10127         N                                  NA
## 120    10120         N                           0.7037805
## 182    10182         N                           0.6858467
## 103    10103         N                                  NA
##     sold.fctr.predict.All.X.glmnet sold.fctr.predict.All.X.glmnet.err
## 15                               N                               TRUE
## 27                            <NA>                                 NA
## 2                                Y                              FALSE
## 23                            <NA>                                 NA
## 195                              N                              FALSE
## 1                                N                              FALSE
## 22                               Y                               TRUE
## 20                               Y                               TRUE
## 135                              Y                               TRUE
## 127                           <NA>                                 NA
## 120                              Y                               TRUE
## 182                              Y                               TRUE
## 103                           <NA>                                 NA
##     sold.fctr.predict.All.X.glmnet.err.abs
## 15                             0.761270521
## 27                                      NA
## 2                              0.002368685
## 23                                      NA
## 195                            0.199145222
## 1                              0.277165051
## 22                             0.530551453
## 20                             0.549285373
## 135                            0.603236741
## 127                                     NA
## 120                            0.703780514
## 182                            0.685846730
## 103                                     NA
##     sold.fctr.predict.All.X.glmnet.accurate
## 15                                    FALSE
## 27                                       NA
## 2                                      TRUE
## 23                                       NA
## 195                                    TRUE
## 1                                      TRUE
## 22                                    FALSE
## 20                                    FALSE
## 135                                   FALSE
## 127                                      NA
## 120                                   FALSE
## 182                                   FALSE
## 103                                      NA
##     sold.fctr.predict.Final.glmnet.prob sold.fctr.predict.Final.glmnet
## 15                            0.2374065                              N
## 27                            0.2891283                              N
## 2                             0.9867924                              Y
## 23                            0.3006404                              Y
## 195                           0.3549341                              Y
## 1                             0.4037304                              Y
## 22                            0.5224896                              Y
## 20                            0.5280866                              Y
## 135                           0.5554621                              Y
## 127                           0.6162385                              Y
## 120                           0.6390285                              Y
## 182                           0.6865033                              Y
## 103                           0.9114699                              Y
##     sold.fctr.predict.Final.glmnet.err
## 15                                TRUE
## 27                                TRUE
## 2                                FALSE
## 23                                TRUE
## 195                               TRUE
## 1                                 TRUE
## 22                                TRUE
## 20                                TRUE
## 135                               TRUE
## 127                               TRUE
## 120                               TRUE
## 182                               TRUE
## 103                               TRUE
##     sold.fctr.predict.Final.glmnet.err.abs
## 15                              0.76259347
## 27                              0.71087169
## 2                               0.01320757
## 23                              0.30064037
## 195                             0.35493408
## 1                               0.40373038
## 22                              0.52248959
## 20                              0.52808658
## 135                             0.55546206
## 127                             0.61623850
## 120                             0.63902847
## 182                             0.68650334
## 103                             0.91146990
##     sold.fctr.predict.Final.glmnet.accurate
## 15                                    FALSE
## 27                                    FALSE
## 2                                      TRUE
## 23                                    FALSE
## 195                                   FALSE
## 1                                     FALSE
## 22                                    FALSE
## 20                                    FALSE
## 135                                   FALSE
## 127                                   FALSE
## 120                                   FALSE
## 182                                   FALSE
## 103                                   FALSE
##     sold.fctr.predict.Final.glmnet.error .label
## 15                          -0.062593475  10015
## 27                          -0.010871691  10027
## 2                            0.000000000  10002
## 23                           0.000640367  10023
## 195                          0.054934084  10195
## 1                            0.103730378  10001
## 22                           0.222489585  10022
## 20                           0.228086584  10020
## 135                          0.255462063  10135
## 127                          0.316238503  10127
## 120                          0.339028470  10120
## 182                          0.386503340  10182
## 103                          0.611469896  10103
## [1] "Inaccurate: "
##      UniqueID sold.fctr sold.fctr.predict.All.X.glmnet.prob
## 1807    11817         Y                                  NA
## 1688    11697         Y                         0.003639011
## 1696    11705         Y                                  NA
## 1352    11359         Y                         0.038703606
## 332     10332         Y                                  NA
## 1113    11119         Y                         0.090092153
##      sold.fctr.predict.All.X.glmnet sold.fctr.predict.All.X.glmnet.err
## 1807                           <NA>                                 NA
## 1688                              N                               TRUE
## 1696                           <NA>                                 NA
## 1352                              N                               TRUE
## 332                            <NA>                                 NA
## 1113                              N                               TRUE
##      sold.fctr.predict.All.X.glmnet.err.abs
## 1807                                     NA
## 1688                              0.9963610
## 1696                                     NA
## 1352                              0.9612964
## 332                                      NA
## 1113                              0.9099078
##      sold.fctr.predict.All.X.glmnet.accurate
## 1807                                      NA
## 1688                                   FALSE
## 1696                                      NA
## 1352                                   FALSE
## 332                                       NA
## 1113                                   FALSE
##      sold.fctr.predict.Final.glmnet.prob sold.fctr.predict.Final.glmnet
## 1807                          0.03420561                              N
## 1688                          0.03456082                              N
## 1696                          0.06394171                              N
## 1352                          0.06497112                              N
## 332                           0.06545247                              N
## 1113                          0.07378931                              N
##      sold.fctr.predict.Final.glmnet.err
## 1807                               TRUE
## 1688                               TRUE
## 1696                               TRUE
## 1352                               TRUE
## 332                                TRUE
## 1113                               TRUE
##      sold.fctr.predict.Final.glmnet.err.abs
## 1807                              0.9657944
## 1688                              0.9654392
## 1696                              0.9360583
## 1352                              0.9350289
## 332                               0.9345475
## 1113                              0.9262107
##      sold.fctr.predict.Final.glmnet.accurate
## 1807                                   FALSE
## 1688                                   FALSE
## 1696                                   FALSE
## 1352                                   FALSE
## 332                                    FALSE
## 1113                                   FALSE
##      sold.fctr.predict.Final.glmnet.error
## 1807                           -0.2657944
## 1688                           -0.2654392
## 1696                           -0.2360583
## 1352                           -0.2350289
## 332                            -0.2345475
## 1113                           -0.2262107
##      UniqueID sold.fctr sold.fctr.predict.All.X.glmnet.prob
## 1430    11437         N                                  NA
## 925     10928         N                                  NA
## 787     10788         N                           0.5234147
## 1043    11046         N                           0.3904027
## 1586    11595         N                           0.4562730
## 1665    11674         N                                  NA
##      sold.fctr.predict.All.X.glmnet sold.fctr.predict.All.X.glmnet.err
## 1430                           <NA>                                 NA
## 925                            <NA>                                 NA
## 787                               Y                               TRUE
## 1043                              Y                               TRUE
## 1586                              Y                               TRUE
## 1665                           <NA>                                 NA
##      sold.fctr.predict.All.X.glmnet.err.abs
## 1430                                     NA
## 925                                      NA
## 787                               0.5234147
## 1043                              0.3904027
## 1586                              0.4562730
## 1665                                     NA
##      sold.fctr.predict.All.X.glmnet.accurate
## 1430                                      NA
## 925                                       NA
## 787                                    FALSE
## 1043                                   FALSE
## 1586                                   FALSE
## 1665                                      NA
##      sold.fctr.predict.Final.glmnet.prob sold.fctr.predict.Final.glmnet
## 1430                           0.3260384                              Y
## 925                            0.3293791                              Y
## 787                            0.3559182                              Y
## 1043                           0.3730374                              Y
## 1586                           0.3798302                              Y
## 1665                           0.9076423                              Y
##      sold.fctr.predict.Final.glmnet.err
## 1430                               TRUE
## 925                                TRUE
## 787                                TRUE
## 1043                               TRUE
## 1586                               TRUE
## 1665                               TRUE
##      sold.fctr.predict.Final.glmnet.err.abs
## 1430                              0.3260384
## 925                               0.3293791
## 787                               0.3559182
## 1043                              0.3730374
## 1586                              0.3798302
## 1665                              0.9076423
##      sold.fctr.predict.Final.glmnet.accurate
## 1430                                   FALSE
## 925                                    FALSE
## 787                                    FALSE
## 1043                                   FALSE
## 1586                                   FALSE
## 1665                                   FALSE
##      sold.fctr.predict.Final.glmnet.error
## 1430                           0.02603841
## 925                            0.02937913
## 787                            0.05591819
## 1043                           0.07303739
## 1586                           0.07983023
## 1665                           0.60764233
##      UniqueID sold.fctr sold.fctr.predict.All.X.glmnet.prob
## 1703    11712         N                                  NA
## 1384    11391         N                                  NA
## 490     10491         N                                  NA
## 1760    11769         N                           0.9523485
## 1682    11691         N                           0.9822811
## 1464    11471         N                                  NA
##      sold.fctr.predict.All.X.glmnet sold.fctr.predict.All.X.glmnet.err
## 1703                           <NA>                                 NA
## 1384                           <NA>                                 NA
## 490                            <NA>                                 NA
## 1760                              Y                               TRUE
## 1682                              Y                               TRUE
## 1464                           <NA>                                 NA
##      sold.fctr.predict.All.X.glmnet.err.abs
## 1703                                     NA
## 1384                                     NA
## 490                                      NA
## 1760                              0.9523485
## 1682                              0.9822811
## 1464                                     NA
##      sold.fctr.predict.All.X.glmnet.accurate
## 1703                                      NA
## 1384                                      NA
## 490                                       NA
## 1760                                   FALSE
## 1682                                   FALSE
## 1464                                      NA
##      sold.fctr.predict.Final.glmnet.prob sold.fctr.predict.Final.glmnet
## 1703                           0.9265818                              Y
## 1384                           0.9305841                              Y
## 490                            0.9403422                              Y
## 1760                           0.9467505                              Y
## 1682                           0.9649070                              Y
## 1464                           0.9689273                              Y
##      sold.fctr.predict.Final.glmnet.err
## 1703                               TRUE
## 1384                               TRUE
## 490                                TRUE
## 1760                               TRUE
## 1682                               TRUE
## 1464                               TRUE
##      sold.fctr.predict.Final.glmnet.err.abs
## 1703                              0.9265818
## 1384                              0.9305841
## 490                               0.9403422
## 1760                              0.9467505
## 1682                              0.9649070
## 1464                              0.9689273
##      sold.fctr.predict.Final.glmnet.accurate
## 1703                                   FALSE
## 1384                                   FALSE
## 490                                    FALSE
## 1760                                   FALSE
## 1682                                   FALSE
## 1464                                   FALSE
##      sold.fctr.predict.Final.glmnet.error
## 1703                            0.6265818
## 1384                            0.6305841
## 490                             0.6403422
## 1760                            0.6467505
## 1682                            0.6649070
## 1464                            0.6689273

dsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
    dsp_feats_vctr <- union(dsp_feats_vctr, 
                            glb_feats_df[!is.na(glb_feats_df[, var]), "id"])

# print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids, 
#                     grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])

print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
## [1] "sold.fctr.predict.Final.glmnet.prob"    
## [2] "sold.fctr.predict.Final.glmnet"         
## [3] "sold.fctr.predict.Final.glmnet.err"     
## [4] "sold.fctr.predict.Final.glmnet.err.abs" 
## [5] "sold.fctr.predict.Final.glmnet.accurate"
for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
    # Merge or cbind ?
    glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]

print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))
## character(0)
print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
## character(0)
for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
    # Merge or cbind ?
    glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
    
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))
## character(0)
if (glb_save_envir)
    save(glb_feats_df, glb_allobs_df, 
         #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
         glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
         glb_sel_mdl, glb_sel_mdl_id,
         glb_fin_mdl, glb_fin_mdl_id,
        file=paste0(glb_out_pfx, "dsk.RData"))

replay.petrisim(pn=glb_analytics_pn, 
    replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "data.training.all.prediction","model.final")), flip_coord=TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0 
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction   firing:  model.selected 
## 3.0000    3   0 2 1 0 
## 3.0000   multiple enabled transitions:  model.final data.training.all.prediction data.new.prediction     firing:  data.training.all.prediction 
## 4.0000    5   0 1 1 1 
## 4.0000   multiple enabled transitions:  model.final data.training.all.prediction data.new.prediction     firing:  model.final 
## 5.0000    4   0 0 2 1

glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)
##                label step_major step_minor label_minor     bgn     end
## 15 fit.data.training          7          1           1 533.211 541.249
## 16  predict.data.new          8          0           0 541.250      NA
##    elapsed
## 15   8.038
## 16      NA

Step 8.0: predict data new

# Compute final model predictions

#glb_to_sav(); all.equal(sav_allobs_df, glb_allobs_df); all.equal(sav_trnobs_df, glb_trnobs_df); all.equal(sav_newobs_df, glb_newobs_df)  
if (glb_is_classification && glb_is_binomial)
    prob_threshold_def <- 
        glb_models_df[glb_models_df$id == glb_sel_mdl_id, "opt.prob.threshold.OOB"] else
    prob_threshold_def <- NULL
for (obsSet in c("trn", "new")) {
    obs_df <- switch(obsSet, all = glb_allobs_df, trn = glb_trnobs_df, new = glb_newobs_df)
    obs_df <- glb_get_predictions(obs_df, mdl_id = glb_fin_mdl_id, 
                    rsp_var_out = glb_rsp_var_out, prob_threshold_def = prob_threshold_def)
    if (obsSet == "all") glb_allobs_df <- obs_df else
    if (obsSet == "trn") glb_trnobs_df <- obs_df else
    if (obsSet == "new") glb_newobs_df <- obs_df
}
## Warning in glb_get_predictions(obs_df, mdl_id = glb_fin_mdl_id, rsp_var_out
## = glb_rsp_var_out, : Using default probability threshold: 0.3
## Warning in glb_get_predictions(obs_df, mdl_id = glb_fin_mdl_id, rsp_var_out
## = glb_rsp_var_out, : Using default probability threshold: 0.3
rm(obs_df)
glb_allobs_df <- orderBy(reformulate(glb_id_var), myrbind_df(glb_trnobs_df, glb_newobs_df))

glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id = glb_fin_mdl_id, 
                         prob_threshold = prob_threshold_def)
## Warning in glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 32
## Warning: Removed 798 rows containing missing values (geom_point).
## Warning: Removed 798 rows containing missing values (geom_point).

## Warning: Removed 798 rows containing missing values (geom_point).
## Warning: Removed 798 rows containing missing values (geom_point).

## Warning: Removed 798 rows containing missing values (geom_point).
## Warning: Removed 798 rows containing missing values (geom_point).

## Warning: Removed 798 rows containing missing values (geom_point).
## Warning: Removed 798 rows containing missing values (geom_point).

## Warning: Removed 798 rows containing missing values (geom_point).
## Warning: Removed 798 rows containing missing values (geom_point).

## [1] "Min/Max Boundaries: "
##      UniqueID sold.fctr sold.fctr.predict.Final.glmnet.prob
## 1851    11862      <NA>                           0.4737074
## 1854    11865      <NA>                           0.8390796
##      sold.fctr.predict.Final.glmnet sold.fctr.predict.Final.glmnet.err
## 1851                              Y                                 NA
## 1854                              Y                                 NA
##      sold.fctr.predict.Final.glmnet.err.abs
## 1851                                     NA
## 1854                                     NA
##      sold.fctr.predict.Final.glmnet.accurate
## 1851                                      NA
## 1854                                      NA
##      sold.fctr.predict.Final.glmnet.error .label
## 1851                                    0  11862
## 1854                                    0  11865
## [1] "Inaccurate: "
##      UniqueID sold.fctr sold.fctr.predict.Final.glmnet.prob
## NA         NA      <NA>                                  NA
## NA.1       NA      <NA>                                  NA
## NA.2       NA      <NA>                                  NA
## NA.3       NA      <NA>                                  NA
## NA.4       NA      <NA>                                  NA
## NA.5       NA      <NA>                                  NA
##      sold.fctr.predict.Final.glmnet sold.fctr.predict.Final.glmnet.err
## NA                             <NA>                                 NA
## NA.1                           <NA>                                 NA
## NA.2                           <NA>                                 NA
## NA.3                           <NA>                                 NA
## NA.4                           <NA>                                 NA
## NA.5                           <NA>                                 NA
##      sold.fctr.predict.Final.glmnet.err.abs
## NA                                       NA
## NA.1                                     NA
## NA.2                                     NA
## NA.3                                     NA
## NA.4                                     NA
## NA.5                                     NA
##      sold.fctr.predict.Final.glmnet.accurate
## NA                                        NA
## NA.1                                      NA
## NA.2                                      NA
## NA.3                                      NA
## NA.4                                      NA
## NA.5                                      NA
##      sold.fctr.predict.Final.glmnet.error
## NA                                     NA
## NA.1                                   NA
## NA.2                                   NA
## NA.3                                   NA
## NA.4                                   NA
## NA.5                                   NA
##        UniqueID sold.fctr sold.fctr.predict.Final.glmnet.prob
## NA.6         NA      <NA>                                  NA
## NA.227       NA      <NA>                                  NA
## NA.241       NA      <NA>                                  NA
## NA.380       NA      <NA>                                  NA
## NA.446       NA      <NA>                                  NA
## NA.507       NA      <NA>                                  NA
##        sold.fctr.predict.Final.glmnet sold.fctr.predict.Final.glmnet.err
## NA.6                             <NA>                                 NA
## NA.227                           <NA>                                 NA
## NA.241                           <NA>                                 NA
## NA.380                           <NA>                                 NA
## NA.446                           <NA>                                 NA
## NA.507                           <NA>                                 NA
##        sold.fctr.predict.Final.glmnet.err.abs
## NA.6                                       NA
## NA.227                                     NA
## NA.241                                     NA
## NA.380                                     NA
## NA.446                                     NA
## NA.507                                     NA
##        sold.fctr.predict.Final.glmnet.accurate
## NA.6                                        NA
## NA.227                                      NA
## NA.241                                      NA
## NA.380                                      NA
## NA.446                                      NA
## NA.507                                      NA
##        sold.fctr.predict.Final.glmnet.error
## NA.6                                     NA
## NA.227                                   NA
## NA.241                                   NA
## NA.380                                   NA
## NA.446                                   NA
## NA.507                                   NA
##        UniqueID sold.fctr sold.fctr.predict.Final.glmnet.prob
## NA.792       NA      <NA>                                  NA
## NA.793       NA      <NA>                                  NA
## NA.794       NA      <NA>                                  NA
## NA.795       NA      <NA>                                  NA
## NA.796       NA      <NA>                                  NA
## NA.797       NA      <NA>                                  NA
##        sold.fctr.predict.Final.glmnet sold.fctr.predict.Final.glmnet.err
## NA.792                           <NA>                                 NA
## NA.793                           <NA>                                 NA
## NA.794                           <NA>                                 NA
## NA.795                           <NA>                                 NA
## NA.796                           <NA>                                 NA
## NA.797                           <NA>                                 NA
##        sold.fctr.predict.Final.glmnet.err.abs
## NA.792                                     NA
## NA.793                                     NA
## NA.794                                     NA
## NA.795                                     NA
## NA.796                                     NA
## NA.797                                     NA
##        sold.fctr.predict.Final.glmnet.accurate
## NA.792                                      NA
## NA.793                                      NA
## NA.794                                      NA
## NA.795                                      NA
## NA.796                                      NA
## NA.797                                      NA
##        sold.fctr.predict.Final.glmnet.error
## NA.792                                   NA
## NA.793                                   NA
## NA.794                                   NA
## NA.795                                   NA
## NA.796                                   NA
## NA.797                                   NA
## Warning: Removed 798 rows containing missing values (geom_point).

if (is.null(glb_out_obs)) obs_df <- glb_newobs_df else
    obs_df <- switch(glb_out_obs, 
                     all = glb_allobs_df, trn = glb_trnobs_df, new = glb_newobs_df)

require(stringr)
if (glb_is_classification && glb_is_binomial) {
#     submit_df <- glb_newobs_df[, c(glb_id_var, 
#                                    paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
#     names(submit_df)[2] <- "Probability1"
    obsout_df <- obs_df[, glb_id_var, FALSE]
    for (clmn in names(glb_out_vars_lst))
        if (!grepl("^%<d-%", glb_out_vars_lst[[clmn]]))
            obsout_df[, clmn] <- obs_df[, glb_out_vars_lst[[clmn]]] else {
            feat <- str_trim(unlist(strsplit(glb_out_vars_lst[[clmn]], "%<d-%"))[2])
            obsout_df[, clmn] <- obs_df[, eval(parse(text = feat))]
        }                                        
    
    glb_force_prediction_lst <- list()
    glb_force_prediction_lst[["0"]] <- c(11885, 11907, 11932, 11943, 
                                         12050, 12115, 12171, 
                                         12253, 12285, 12367, 12388, 12399,
                                         12585)
    for (obs_id in glb_force_prediction_lst[["0"]]) {
        if (sum(glb_allobs_df[, glb_id_var] == obs_id) == 0)
            next
        if (is.na(glb_allobs_df[glb_allobs_df[, glb_id_var] == obs_id, ".grpid"]))
            stop(".grpid is NA")
#         submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
#             max(0, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] - 0.5)
    }
    
    glb_force_prediction_lst[["1"]] <- c(11871, 11875, 11886, 
                        11913, 11931, 11937, 11967, 11982, 11990, 11991, 11994, 11999,
                                      12000, 12002, 12004, 12018, 12021, 12065, 12072,
                                         12111, 12114, 12126, 12134, 12152, 12172,
                                         12213, 12214, 12233, 12265, 12278, 12299, 
                                         12446, 12491, 
                                         12505, 12576, 12608, 12630)
    for (obs_id in glb_force_prediction_lst[["1"]]) {
        if (sum(glb_allobs_df[, glb_id_var] == obs_id) == 0)
            next
        if (is.na(glb_allobs_df[glb_allobs_df[, glb_id_var] == obs_id, ".grpid"]))
            stop(".grpid is NA")
#         submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
#             min(0.9999, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] + 0.5)
    }
    
    rsp_var_out <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
    for (obs_id in glb_newobs_df[!is.na(glb_newobs_df[, rsp_var_out]) & 
                                 (glb_newobs_df[, rsp_var_out] == "Y") & 
                                 (glb_newobs_df[ , "startprice"] > 675), "UniqueID"]) {
#         submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
#             max(0, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] - 0.5)
    }    
} else {
#     submit_df <- glb_newobs_df[, c(glb_id_var, 
#                                    paste0(glb_rsp_var_out, glb_fin_mdl_id))]
    obsout_df <- obs_df[, glb_id_var, FALSE]
    for (clmn in names(glb_out_vars_lst))
        if (!grepl("^%<d-%", glb_out_vars_lst[[clmn]]))
            obsout_df[, clmn] <- obs_df[, glb_out_vars_lst[[clmn]]] else {
            feat <- str_trim(unlist(strsplit(glb_out_vars_lst[[clmn]], "%<d-%"))[2])
            obsout_df[, clmn] <- obs_df[, eval(parse(text=feat))]
        }                                        
}    

if (glb_is_classification) {
    rsp_var_out <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
    tmp_newobs_df <- subset(glb_newobs_df[, c(glb_id_var, ".grpid", rsp_var_out)],
                            !is.na(.grpid))
    tmp_newobs_df <- merge(tmp_newobs_df, dupgrps_df, by = ".grpid", all.x = TRUE)
    tmp_newobs_df <- merge(tmp_newobs_df, obsout_df, by = glb_id_var, all.x = TRUE)
    tmp_newobs_df$.err <- 
        ((tmp_newobs_df$Probability1 > 0.5) & (tmp_newobs_df$sold.0 > 0) |
         (tmp_newobs_df$Probability1 < 0.5) & (tmp_newobs_df$sold.1 > 0))
    tmp_newobs_df <- orderBy(~UniqueID, subset(tmp_newobs_df, .err == TRUE))
    print(sprintf("Prediction errors in duplicates: %d", nrow(tmp_newobs_df)))
    print(tmp_newobs_df)
    
#     if (nrow(tmp_newobs_df) > 0)
#         stop("check Prediction errors in duplicates")
    #print(dupobs_df[dupobs_df$.grpid == 26, ])
    
    tmp_newobs_df <- cbind(glb_newobs_df, obsout_df[, "Probability1", FALSE])
#     if (max(glb_newobs_df[!is.na(glb_newobs_df[, rsp_var_out]) & 
#                       (tmp_newobs_df[, "Probability1"] >= 0.5), "startprice"]) > 
#         max(glb_allobs_df[!is.na(glb_allobs_df[, glb_rsp_var]) & 
#                       (glb_allobs_df[, glb_rsp_var] == "Y"), "startprice"]))
#         stop("startprice for some +ve predictions > 675")
    
    # Check predictions that are outside of data ranges
#stop(here")
    require(stringr)
    tmp_feats_df <- subset(glb_feats_df, 
                           !nzv & 
                            (exclude.as.feat != 1) & 
                            !grepl(".fctr", id, fixed=TRUE))[, "id", FALSE]
    ranges_all_df <- glb_allobs_df[, tmp_feats_df$id] %>% 
                        dplyr::summarise_each(funs(min(., na.rm=TRUE), 
                                                   max(., na.rm=TRUE))) %>%
                        tidyr::gather() %>%
                        dplyr::mutate(id=str_sub(key, 1, -5), 
                                      stat=str_sub(key, -3)) %>% 
                        dplyr::select(-key) %>%
                        tidyr::spread(stat, value)
    
#     sav_ranges_trn_df <- ranges_trn_df; all.equal(sav_ranges_trn_df, ranges_trn_df)
#     sav_ranges_new_df <- ranges_new_df; all.equal(sav_ranges_new_df, ranges_new_df)    
    get_ranges_df <- function(obs_df, feats, class_var) {
        require(tidyr)
        ranges_df <- obs_df[, c(class_var, feats)] %>% 
            dplyr::group_by_(class_var) %>%
            dplyr::summarise_each(funs(min(., na.rm=TRUE), 
                                       max(., na.rm=TRUE))) %>%
            tidyr::gather(key, value, -1) %>%
            mutate(id=str_sub(key, 1, -5), 
                   stat.vname=paste0(str_sub(key, -3), ".", class_var)) %>%
            unite_("stat.class", c("stat.vname", class_var), sep=".") %>% 
            dplyr::select(-key) %>%
            spread(stat.class, value)
        return(ranges_df)
    }
    rsp_var_out_OOB <- paste0(glb_rsp_var_out, glb_sel_mdl_id)
    rsp_var_out_new <- paste0(glb_rsp_var_out, glb_fin_mdl_id)    
    ranges_trn_df <- get_ranges_df(obs_df=glb_trnobs_df, feats=tmp_feats_df$id, 
                                   class_var=glb_rsp_var)
    ranges_fit_df <- get_ranges_df(obs_df=glb_fitobs_df, feats=tmp_feats_df$id, 
                                   class_var=glb_rsp_var)
    ranges_OOB_df <- get_ranges_df(obs_df=glb_OOBobs_df, feats=tmp_feats_df$id, 
                                   class_var=rsp_var_out_OOB)
    ranges_new_df <- get_ranges_df(obs_df=glb_newobs_df, feats=tmp_feats_df$id, 
                                   class_var=rsp_var_out_new)

    for (obsset in c("OOB", "new")) {
        if (obsset == "OOB") { 
            ranges_ref_df <- ranges_fit_df; obs_df <- glb_OOBobs_df; 
            rsp_var_out_obs <- rsp_var_out_OOB; sprintf_pfx <- "OOBobs";
        } else { 
            ranges_ref_df <- ranges_trn_df; obs_df <- glb_newobs_df; 
            rsp_var_out_obs <- rsp_var_out_new; sprintf_pfx <- "newobs"; 
        }
        plt_feats_df <- glb_feats_df %>% 
                            merge(ranges_all_df, all=TRUE) %>%
                            merge(ranges_ref_df, all=TRUE) %>%
                            merge(ranges_OOB_df, all=TRUE) %>%        
                            merge(ranges_new_df, all=TRUE) %>%
                            subset(!is.na(min) & (id != ".rnorm"))
        row.names(plt_feats_df) <- plt_feats_df$id
        range_outlier_ids <- c(NULL)
        for (clss in unique(obs_df[, rsp_var_out_obs])) {
            for (stat in c("min", "max")) {
                if (stat == "min") {
                    dsp_feats <- plt_feats_df[
                            which(plt_feats_df[, paste("min", rsp_var_out_obs, clss, sep=".")] < 
                                  plt_feats_df[, paste("min", glb_rsp_var, clss, sep=".")]), "id"]
                } else {
                    dsp_feats <- plt_feats_df[
                            which(plt_feats_df[, paste("max", rsp_var_out_obs, clss, sep=".")] > 
                                  plt_feats_df[, paste("max", glb_rsp_var, clss, sep=".")]), "id"]
                }
                if (length(dsp_feats) > 0) {
                    ths_ids <- c(NULL)
                    for (feat in dsp_feats) {
                        if (stat == "min") {
                            ths_ids <- union(ths_ids, 
                                             obs_df[(obs_df[, rsp_var_out_obs] == clss) &
                                                           (obs_df[, feat] < 
                plt_feats_df[plt_feats_df$id == feat, paste("min", glb_rsp_var, clss, sep=".")]), 
                                                            glb_id_var])
                        } else {
                        ths_ids <- union(ths_ids, 
                                             obs_df[(obs_df[, rsp_var_out_obs] == clss) &
                                                           (obs_df[, feat] > 
                plt_feats_df[plt_feats_df$id == feat, paste("max", glb_rsp_var, clss, sep=".")]), 
                                                            glb_id_var])
                        }
                    }
                    tmp_obs_df <- obs_df[obs_df[, glb_id_var] %in% ths_ids, 
                                                   c(glb_id_var, rsp_var_out_obs, dsp_feats)]
                    if (stat == "min") {
                        print(sprintf("%s %s %s: min < min of Train range: %d", 
                                      sprintf_pfx, rsp_var_out_obs, clss, nrow(tmp_obs_df)))
                    } else {
                        print(sprintf("%s %s %s: max > max of Train range: %d", 
                                      sprintf_pfx, rsp_var_out_obs, clss, nrow(tmp_obs_df)))
                    }
                    myprint_df(tmp_obs_df)
                    print(subset(plt_feats_df, id %in% dsp_feats))
                    
                    range_outlier_ids <- union(range_outlier_ids, ths_ids)
                }
            }
        }
        print(sprintf("%s total range outliers: %d", sprintf_pfx, length(range_outlier_ids)))
    }
}    
## [1] "Prediction errors in duplicates: 15"
##    UniqueID .grpid sold.fctr.predict.Final.glmnet sold.0 sold.1 sold.NA
## 4     11886    134                              Y      0      1       1
## 9     11931     53                              Y      0      2       2
## 23    11994    101                              Y      0      1       1
## 24    11999    115                              Y      0      1       1
## 42    12111    128                              Y      0      1       1
## 44    12115     56                              Y      1      0       1
## 59    12233     30                              Y      0      1       1
## 68    12285     55                              Y      1      0       2
## 69    12299     53                              Y      0      2       2
## 72    12367     55                              Y      1      0       2
## 76    12446    103                              N      0      1       1
## 80    12505     54                              Y      0      1       1
## 81    12576     93                              N      0      1       1
## 82    12585    112                              Y      1      0       1
## 87    12630     76                              Y      0      2       1
##    .freq Probability1 .err
## 4      2    0.3527705 TRUE
## 9      4    0.4356090 TRUE
## 23     2    0.4130907 TRUE
## 24     2    0.4077973 TRUE
## 42     2    0.4360890 TRUE
## 44     2    0.5274436 TRUE
## 59     2    0.3347090 TRUE
## 68     3    0.5633672 TRUE
## 69     4    0.4356090 TRUE
## 72     3    0.5633672 TRUE
## 76     2    0.2123043 TRUE
## 80     2    0.4246938 TRUE
## 81     2    0.2095837 TRUE
## 82     2    0.5577166 TRUE
## 87     3    0.3397270 TRUE
## [1] "OOBobs sold.fctr.predict.All.X.glmnet N: max > max of Train range: 6"
##      UniqueID sold.fctr.predict.All.X.glmnet D.chrs.uppr.n.log
## 1294    11301                              N          4.465908
## 1117    11123                              N          4.343805
## 1633    11642                              N          2.708050
## 1806    11816                              N          4.382027
## 1767    11776                              N          3.850148
## 1810    11820                              N          4.382027
##      D.terms.post.stem.n.log D.weight.post.stem.sum D.weight.post.stop.sum
## 1294                2.564949               4.522716               5.268255
## 1117                2.639057               6.824967               7.020585
## 1633                1.098612               6.917242               6.917242
## 1806                2.995732               4.193223               4.241549
## 1767                2.302585               7.257702               7.278648
## 1810                2.995732               4.193223               4.241549
##      D.weight.sum D.wrds.unq.n.log
## 1294     4.522716         2.564949
## 1117     6.824967         2.639057
## 1633     6.917242         1.098612
## 1806     4.193223         2.995732
## 1767     7.257702         2.302585
## 1810     4.193223         2.995732
##                                              id       cor.y
## D.chrs.uppr.n.log             D.chrs.uppr.n.log -0.05449161
## D.terms.post.stem.n.log D.terms.post.stem.n.log -0.06222063
## D.weight.post.stem.sum   D.weight.post.stem.sum -0.04710544
## D.weight.post.stop.sum   D.weight.post.stop.sum -0.04500921
## D.weight.sum                       D.weight.sum -0.04710544
## D.wrds.unq.n.log               D.wrds.unq.n.log -0.06222063
##                         exclude.as.feat  cor.y.abs              cor.high.X
## D.chrs.uppr.n.log                 FALSE 0.05449161            D.chrs.n.log
## D.terms.post.stem.n.log           FALSE 0.06222063 D.terms.post.stop.n.log
## D.weight.post.stem.sum            FALSE 0.04710544 D.terms.post.stop.n.log
## D.weight.post.stop.sum            FALSE 0.04500921  D.weight.post.stem.sum
## D.weight.sum                      FALSE 0.04710544  D.weight.post.stem.sum
## D.wrds.unq.n.log                  FALSE 0.06222063 D.terms.post.stem.n.log
##                         freqRatio percentUnique zeroVar   nzv
## D.chrs.uppr.n.log        15.66176      4.486486   FALSE FALSE
## D.terms.post.stem.n.log  11.71429      1.135135   FALSE FALSE
## D.weight.post.stem.sum   62.70588     34.648649   FALSE FALSE
## D.weight.post.stop.sum   62.70588     34.756757   FALSE FALSE
## D.weight.sum             62.70588     34.648649   FALSE FALSE
## D.wrds.unq.n.log         11.71429      1.135135   FALSE FALSE
##                         is.cor.y.abs.low interaction.feat
## D.chrs.uppr.n.log                  FALSE             <NA>
## D.terms.post.stem.n.log            FALSE             <NA>
## D.weight.post.stem.sum             FALSE             <NA>
## D.weight.post.stop.sum             FALSE             <NA>
## D.weight.sum                       FALSE             <NA>
## D.wrds.unq.n.log                   FALSE             <NA>
##                         shapiro.test.p.value rsp_var_raw id_var rsp_var
## D.chrs.uppr.n.log               4.550442e-50       FALSE     NA      NA
## D.terms.post.stem.n.log         1.386439e-48       FALSE     NA      NA
## D.weight.post.stem.sum          2.197748e-48       FALSE     NA      NA
## D.weight.post.stop.sum          2.648991e-48       FALSE     NA      NA
## D.weight.sum                    2.197748e-48       FALSE     NA      NA
## D.wrds.unq.n.log                1.386439e-48       FALSE     NA      NA
##                              max min max.sold.fctr.N max.sold.fctr.Y
## D.chrs.uppr.n.log       4.465908   0        4.454347        4.454347
## D.terms.post.stem.n.log 3.044522   0        2.944439        2.995732
## D.weight.post.stem.sum  8.048759   0        6.702992        7.048759
## D.weight.post.stop.sum  8.563332   0        7.202321        7.048759
## D.weight.sum            8.048759   0        6.702992        7.048759
## D.wrds.unq.n.log        3.044522   0        2.944439        2.995732
##                         min.sold.fctr.N min.sold.fctr.Y
## D.chrs.uppr.n.log                     0               0
## D.terms.post.stem.n.log               0               0
## D.weight.post.stem.sum                0               0
## D.weight.post.stop.sum                0               0
## D.weight.sum                          0               0
## D.wrds.unq.n.log                      0               0
##                         max.sold.fctr.predict.All.X.glmnet.N
## D.chrs.uppr.n.log                                   4.465908
## D.terms.post.stem.n.log                             2.995732
## D.weight.post.stem.sum                              7.257702
## D.weight.post.stop.sum                              7.278648
## D.weight.sum                                        7.257702
## D.wrds.unq.n.log                                    2.995732
##                         max.sold.fctr.predict.All.X.glmnet.Y
## D.chrs.uppr.n.log                                   4.442651
## D.terms.post.stem.n.log                             3.044522
## D.weight.post.stem.sum                              8.048759
## D.weight.post.stop.sum                              8.563332
## D.weight.sum                                        8.048759
## D.wrds.unq.n.log                                    3.044522
##                         min.sold.fctr.predict.All.X.glmnet.N
## D.chrs.uppr.n.log                                          0
## D.terms.post.stem.n.log                                    0
## D.weight.post.stem.sum                                     0
## D.weight.post.stop.sum                                     0
## D.weight.sum                                               0
## D.wrds.unq.n.log                                           0
##                         min.sold.fctr.predict.All.X.glmnet.Y
## D.chrs.uppr.n.log                                          0
## D.terms.post.stem.n.log                                    0
## D.weight.post.stem.sum                                     0
## D.weight.post.stop.sum                                     0
## D.weight.sum                                               0
## D.wrds.unq.n.log                                           0
##                         max.sold.fctr.predict.Final.glmnet.N
## D.chrs.uppr.n.log                                   4.465908
## D.terms.post.stem.n.log                             2.944439
## D.weight.post.stem.sum                              6.605876
## D.weight.post.stop.sum                              6.682933
## D.weight.sum                                        6.605876
## D.wrds.unq.n.log                                    2.944439
##                         max.sold.fctr.predict.Final.glmnet.Y
## D.chrs.uppr.n.log                                   4.442651
## D.terms.post.stem.n.log                             2.944439
## D.weight.post.stem.sum                              6.253320
## D.weight.post.stop.sum                              6.851448
## D.weight.sum                                        6.253320
## D.wrds.unq.n.log                                    2.944439
##                         min.sold.fctr.predict.Final.glmnet.N
## D.chrs.uppr.n.log                                          0
## D.terms.post.stem.n.log                                    0
## D.weight.post.stem.sum                                     0
## D.weight.post.stop.sum                                     0
## D.weight.sum                                               0
## D.wrds.unq.n.log                                           0
##                         min.sold.fctr.predict.Final.glmnet.Y
## D.chrs.uppr.n.log                                          0
## D.terms.post.stem.n.log                                    0
## D.weight.post.stem.sum                                     0
## D.weight.post.stop.sum                                     0
## D.weight.sum                                               0
## D.wrds.unq.n.log                                           0
## [1] "OOBobs sold.fctr.predict.All.X.glmnet Y: min < min of Train range: 3"
##      UniqueID sold.fctr.predict.All.X.glmnet D.ratio.wrds.stop.n.wrds.n
## 915     10918                              Y                 0.04347826
## 1594    11603                              Y                 0.40000000
## 227     10227                              Y                 0.30000000
##      D.weight.sum.stem.stop.Ratio
## 915                     0.9756676
## 1594                    0.7143207
## 227                     0.7741412
##                                                        id       cor.y
## D.ratio.wrds.stop.n.wrds.n     D.ratio.wrds.stop.n.wrds.n 0.059547328
## D.weight.sum.stem.stop.Ratio D.weight.sum.stem.stop.Ratio 0.002381527
##                              exclude.as.feat   cor.y.abs
## D.ratio.wrds.stop.n.wrds.n             FALSE 0.059547328
## D.weight.sum.stem.stop.Ratio           FALSE 0.002381527
##                                           cor.high.X freqRatio
## D.ratio.wrds.stop.n.wrds.n   D.terms.post.stop.n.log  18.06780
## D.weight.sum.stem.stop.Ratio                    <NA>  64.47059
##                              percentUnique zeroVar   nzv is.cor.y.abs.low
## D.ratio.wrds.stop.n.wrds.n         4.27027   FALSE FALSE            FALSE
## D.weight.sum.stem.stop.Ratio      33.56757   FALSE FALSE            FALSE
##                              interaction.feat shapiro.test.p.value
## D.ratio.wrds.stop.n.wrds.n               <NA>         5.138767e-49
## D.weight.sum.stem.stop.Ratio             <NA>         7.686585e-54
##                              rsp_var_raw id_var rsp_var max        min
## D.ratio.wrds.stop.n.wrds.n         FALSE     NA      NA   1 0.04347826
## D.weight.sum.stem.stop.Ratio       FALSE     NA      NA   1 0.71432071
##                              max.sold.fctr.N max.sold.fctr.Y
## D.ratio.wrds.stop.n.wrds.n                 1               1
## D.weight.sum.stem.stop.Ratio               1               1
##                              min.sold.fctr.N min.sold.fctr.Y
## D.ratio.wrds.stop.n.wrds.n        0.04347826      0.04545455
## D.weight.sum.stem.stop.Ratio      0.76580062      0.81266745
##                              max.sold.fctr.predict.All.X.glmnet.N
## D.ratio.wrds.stop.n.wrds.n                                      1
## D.weight.sum.stem.stop.Ratio                                    1
##                              max.sold.fctr.predict.All.X.glmnet.Y
## D.ratio.wrds.stop.n.wrds.n                                      1
## D.weight.sum.stem.stop.Ratio                                    1
##                              min.sold.fctr.predict.All.X.glmnet.N
## D.ratio.wrds.stop.n.wrds.n                             0.04761905
## D.weight.sum.stem.stop.Ratio                           0.80128574
##                              min.sold.fctr.predict.All.X.glmnet.Y
## D.ratio.wrds.stop.n.wrds.n                             0.04347826
## D.weight.sum.stem.stop.Ratio                           0.71432071
##                              max.sold.fctr.predict.Final.glmnet.N
## D.ratio.wrds.stop.n.wrds.n                                      1
## D.weight.sum.stem.stop.Ratio                                    1
##                              max.sold.fctr.predict.Final.glmnet.Y
## D.ratio.wrds.stop.n.wrds.n                                      1
## D.weight.sum.stem.stop.Ratio                                    1
##                              min.sold.fctr.predict.Final.glmnet.N
## D.ratio.wrds.stop.n.wrds.n                              0.0500000
## D.weight.sum.stem.stop.Ratio                            0.8220163
##                              min.sold.fctr.predict.Final.glmnet.Y
## D.ratio.wrds.stop.n.wrds.n                              0.0500000
## D.weight.sum.stem.stop.Ratio                            0.7284632
## [1] "OOBobs sold.fctr.predict.All.X.glmnet Y: max > max of Train range: 6"
##      UniqueID sold.fctr.predict.All.X.glmnet D.terms.post.stem.n.log
## 873     10876                              Y               1.3862944
## 1612    11621                              Y               2.3025851
## 915     10918                              Y               3.0445224
## 1594    11603                              Y               1.3862944
## 119     10119                              Y               1.6094379
## 812     10814                              Y               0.6931472
##      D.terms.post.stop.n.log D.weight.post.stem.sum D.weight.post.stop.sum
## 873                1.3862944               7.258891               7.484915
## 1612               2.3025851               4.333960               4.498634
## 915                3.0445224               4.623058               4.738354
## 1594               1.3862944               5.066153               7.092267
## 119                1.6094379               7.332280               7.332280
## 812                0.6931472               8.048759               8.563332
##      D.weight.sum D.wrds.stop.n.log D.wrds.unq.n.log
## 873      7.258891         0.6931472        1.3862944
## 1612     4.333960         2.3025851        2.3025851
## 915      4.623058         0.0000000        3.0445224
## 1594     5.066153         0.6931472        1.3862944
## 119      7.332280         0.6931472        1.6094379
## 812      8.048759         1.0986123        0.6931472
##                                              id        cor.y
## D.terms.post.stem.n.log D.terms.post.stem.n.log -0.062220631
## D.terms.post.stop.n.log D.terms.post.stop.n.log -0.062526435
## D.weight.post.stem.sum   D.weight.post.stem.sum -0.047105442
## D.weight.post.stop.sum   D.weight.post.stop.sum -0.045009208
## D.weight.sum                       D.weight.sum -0.047105442
## D.wrds.stop.n.log             D.wrds.stop.n.log  0.003851183
## D.wrds.unq.n.log               D.wrds.unq.n.log -0.062220631
##                         exclude.as.feat   cor.y.abs
## D.terms.post.stem.n.log           FALSE 0.062220631
## D.terms.post.stop.n.log           FALSE 0.062526435
## D.weight.post.stem.sum            FALSE 0.047105442
## D.weight.post.stop.sum            FALSE 0.045009208
## D.weight.sum                      FALSE 0.047105442
## D.wrds.stop.n.log                 FALSE 0.003851183
## D.wrds.unq.n.log                  FALSE 0.062220631
##                                      cor.high.X freqRatio percentUnique
## D.terms.post.stem.n.log D.terms.post.stop.n.log 11.714286     1.1351351
## D.terms.post.stop.n.log                    <NA> 13.325000     1.1351351
## D.weight.post.stem.sum  D.terms.post.stop.n.log 62.705882    34.6486486
## D.weight.post.stop.sum   D.weight.post.stem.sum 62.705882    34.7567568
## D.weight.sum             D.weight.post.stem.sum 62.705882    34.6486486
## D.wrds.stop.n.log                          <NA>  8.732484     0.5405405
## D.wrds.unq.n.log        D.terms.post.stem.n.log 11.714286     1.1351351
##                         zeroVar   nzv is.cor.y.abs.low interaction.feat
## D.terms.post.stem.n.log   FALSE FALSE            FALSE             <NA>
## D.terms.post.stop.n.log   FALSE FALSE            FALSE             <NA>
## D.weight.post.stem.sum    FALSE FALSE            FALSE             <NA>
## D.weight.post.stop.sum    FALSE FALSE            FALSE             <NA>
## D.weight.sum              FALSE FALSE            FALSE             <NA>
## D.wrds.stop.n.log         FALSE FALSE            FALSE             <NA>
## D.wrds.unq.n.log          FALSE FALSE            FALSE             <NA>
##                         shapiro.test.p.value rsp_var_raw id_var rsp_var
## D.terms.post.stem.n.log         1.386439e-48       FALSE     NA      NA
## D.terms.post.stop.n.log         1.391188e-48       FALSE     NA      NA
## D.weight.post.stem.sum          2.197748e-48       FALSE     NA      NA
## D.weight.post.stop.sum          2.648991e-48       FALSE     NA      NA
## D.weight.sum                    2.197748e-48       FALSE     NA      NA
## D.wrds.stop.n.log               4.444633e-54       FALSE     NA      NA
## D.wrds.unq.n.log                1.386439e-48       FALSE     NA      NA
##                              max min max.sold.fctr.N max.sold.fctr.Y
## D.terms.post.stem.n.log 3.044522   0        2.944439        2.995732
## D.terms.post.stop.n.log 3.044522   0        2.995732        2.995732
## D.weight.post.stem.sum  8.048759   0        6.702992        7.048759
## D.weight.post.stop.sum  8.563332   0        7.202321        7.048759
## D.weight.sum            8.048759   0        6.702992        7.048759
## D.wrds.stop.n.log       2.564949   0        2.302585        2.197225
## D.wrds.unq.n.log        3.044522   0        2.944439        2.995732
##                         min.sold.fctr.N min.sold.fctr.Y
## D.terms.post.stem.n.log               0               0
## D.terms.post.stop.n.log               0               0
## D.weight.post.stem.sum                0               0
## D.weight.post.stop.sum                0               0
## D.weight.sum                          0               0
## D.wrds.stop.n.log                     0               0
## D.wrds.unq.n.log                      0               0
##                         max.sold.fctr.predict.All.X.glmnet.N
## D.terms.post.stem.n.log                             2.995732
## D.terms.post.stop.n.log                             2.995732
## D.weight.post.stem.sum                              7.257702
## D.weight.post.stop.sum                              7.278648
## D.weight.sum                                        7.257702
## D.wrds.stop.n.log                                   2.302585
## D.wrds.unq.n.log                                    2.995732
##                         max.sold.fctr.predict.All.X.glmnet.Y
## D.terms.post.stem.n.log                             3.044522
## D.terms.post.stop.n.log                             3.044522
## D.weight.post.stem.sum                              8.048759
## D.weight.post.stop.sum                              8.563332
## D.weight.sum                                        8.048759
## D.wrds.stop.n.log                                   2.302585
## D.wrds.unq.n.log                                    3.044522
##                         min.sold.fctr.predict.All.X.glmnet.N
## D.terms.post.stem.n.log                                    0
## D.terms.post.stop.n.log                                    0
## D.weight.post.stem.sum                                     0
## D.weight.post.stop.sum                                     0
## D.weight.sum                                               0
## D.wrds.stop.n.log                                          0
## D.wrds.unq.n.log                                           0
##                         min.sold.fctr.predict.All.X.glmnet.Y
## D.terms.post.stem.n.log                                    0
## D.terms.post.stop.n.log                                    0
## D.weight.post.stem.sum                                     0
## D.weight.post.stop.sum                                     0
## D.weight.sum                                               0
## D.wrds.stop.n.log                                          0
## D.wrds.unq.n.log                                           0
##                         max.sold.fctr.predict.Final.glmnet.N
## D.terms.post.stem.n.log                             2.944439
## D.terms.post.stop.n.log                             2.944439
## D.weight.post.stem.sum                              6.605876
## D.weight.post.stop.sum                              6.682933
## D.weight.sum                                        6.605876
## D.wrds.stop.n.log                                   2.397895
## D.wrds.unq.n.log                                    2.944439
##                         max.sold.fctr.predict.Final.glmnet.Y
## D.terms.post.stem.n.log                             2.944439
## D.terms.post.stop.n.log                             2.944439
## D.weight.post.stem.sum                              6.253320
## D.weight.post.stop.sum                              6.851448
## D.weight.sum                                        6.253320
## D.wrds.stop.n.log                                   2.564949
## D.wrds.unq.n.log                                    2.944439
##                         min.sold.fctr.predict.Final.glmnet.N
## D.terms.post.stem.n.log                                    0
## D.terms.post.stop.n.log                                    0
## D.weight.post.stem.sum                                     0
## D.weight.post.stop.sum                                     0
## D.weight.sum                                               0
## D.wrds.stop.n.log                                          0
## D.wrds.unq.n.log                                           0
##                         min.sold.fctr.predict.Final.glmnet.Y
## D.terms.post.stem.n.log                                    0
## D.terms.post.stop.n.log                                    0
## D.weight.post.stem.sum                                     0
## D.weight.post.stop.sum                                     0
## D.weight.sum                                               0
## D.wrds.stop.n.log                                          0
## D.wrds.unq.n.log                                           0
## [1] "OOBobs total range outliers: 13"
## [1] "newobs sold.fctr.predict.Final.glmnet Y: max > max of Train range: 4"
##      UniqueID sold.fctr.predict.Final.glmnet D.chrs.n.log
## 2020    12031                              Y     4.663439
## 2048    12059                              Y     3.737670
## 2076    12087                              Y     4.663439
## 2329    12340                              Y     4.624973
##      D.chrs.pnct13.n.log D.wrds.stop.n.log
## 2020           0.6931472          1.386294
## 2048           2.7080502          0.000000
## 2076           0.0000000          1.098612
## 2329           0.0000000          2.564949
##                                      id        cor.y exclude.as.feat
## D.chrs.n.log               D.chrs.n.log -0.055576665           FALSE
## D.chrs.pnct13.n.log D.chrs.pnct13.n.log -0.032095777           FALSE
## D.wrds.stop.n.log     D.wrds.stop.n.log  0.003851183           FALSE
##                       cor.y.abs              cor.high.X freqRatio
## D.chrs.n.log        0.055576665 D.terms.post.stop.n.log 13.455696
## D.chrs.pnct13.n.log 0.032095777 D.terms.post.stop.n.log  5.272374
## D.wrds.stop.n.log   0.003851183                    <NA>  8.732484
##                     percentUnique zeroVar   nzv is.cor.y.abs.low
## D.chrs.n.log            5.3513514   FALSE FALSE            FALSE
## D.chrs.pnct13.n.log     0.4864865   FALSE FALSE            FALSE
## D.wrds.stop.n.log       0.5405405   FALSE FALSE            FALSE
##                     interaction.feat shapiro.test.p.value rsp_var_raw
## D.chrs.n.log                    <NA>         4.162678e-50       FALSE
## D.chrs.pnct13.n.log             <NA>         1.157759e-53       FALSE
## D.wrds.stop.n.log               <NA>         4.444633e-54       FALSE
##                     id_var rsp_var      max min max.sold.fctr.N
## D.chrs.n.log            NA      NA 4.682131   0        4.682131
## D.chrs.pnct13.n.log     NA      NA 2.708050   0        1.945910
## D.wrds.stop.n.log       NA      NA 2.564949   0        2.302585
##                     max.sold.fctr.Y min.sold.fctr.N min.sold.fctr.Y
## D.chrs.n.log               4.644391               0               0
## D.chrs.pnct13.n.log        2.197225               0               0
## D.wrds.stop.n.log          2.302585               0               0
##                     max.sold.fctr.predict.All.X.glmnet.N
## D.chrs.n.log                                    4.644391
## D.chrs.pnct13.n.log                             1.609438
## D.wrds.stop.n.log                               2.302585
##                     max.sold.fctr.predict.All.X.glmnet.Y
## D.chrs.n.log                                    4.624973
## D.chrs.pnct13.n.log                             1.791759
## D.wrds.stop.n.log                               2.302585
##                     min.sold.fctr.predict.All.X.glmnet.N
## D.chrs.n.log                                           0
## D.chrs.pnct13.n.log                                    0
## D.wrds.stop.n.log                                      0
##                     min.sold.fctr.predict.All.X.glmnet.Y
## D.chrs.n.log                                           0
## D.chrs.pnct13.n.log                                    0
## D.wrds.stop.n.log                                      0
##                     max.sold.fctr.predict.Final.glmnet.N
## D.chrs.n.log                                    4.663439
## D.chrs.pnct13.n.log                             1.791759
## D.wrds.stop.n.log                               2.397895
##                     max.sold.fctr.predict.Final.glmnet.Y
## D.chrs.n.log                                    4.663439
## D.chrs.pnct13.n.log                             2.708050
## D.wrds.stop.n.log                               2.564949
##                     min.sold.fctr.predict.Final.glmnet.N
## D.chrs.n.log                                           0
## D.chrs.pnct13.n.log                                    0
## D.wrds.stop.n.log                                      0
##                     min.sold.fctr.predict.Final.glmnet.Y
## D.chrs.n.log                                           0
## D.chrs.pnct13.n.log                                    0
## D.wrds.stop.n.log                                      0
## [1] "newobs sold.fctr.predict.Final.glmnet N: min < min of Train range: 1"
##      UniqueID sold.fctr.predict.Final.glmnet sprice.d20nexp
## 2614    12625                              N   1.929714e-22
##                            id     cor.y exclude.as.feat cor.y.abs
## sprice.d20nexp sprice.d20nexp 0.3979951           FALSE 0.3979951
##                  cor.high.X freqRatio percentUnique zeroVar   nzv
## sprice.d20nexp sprice.log10  2.807692      30.21622   FALSE FALSE
##                is.cor.y.abs.low interaction.feat shapiro.test.p.value
## sprice.d20nexp            FALSE             <NA>         1.494597e-59
##                rsp_var_raw id_var rsp_var       max          min
## sprice.d20nexp       FALSE     NA      NA 0.9995001 1.929714e-22
##                max.sold.fctr.N max.sold.fctr.Y min.sold.fctr.N
## sprice.d20nexp       0.9517052       0.9995001    2.027639e-22
##                min.sold.fctr.Y max.sold.fctr.predict.All.X.glmnet.N
## sprice.d20nexp    2.200702e-15                            0.8611384
##                max.sold.fctr.predict.All.X.glmnet.Y
## sprice.d20nexp                            0.9995001
##                min.sold.fctr.predict.All.X.glmnet.N
## sprice.d20nexp                         9.484101e-19
##                min.sold.fctr.predict.All.X.glmnet.Y
## sprice.d20nexp                         2.087968e-14
##                max.sold.fctr.predict.Final.glmnet.N
## sprice.d20nexp                            0.7538966
##                max.sold.fctr.predict.Final.glmnet.Y
## sprice.d20nexp                            0.9995001
##                min.sold.fctr.predict.Final.glmnet.N
## sprice.d20nexp                         1.929714e-22
##                min.sold.fctr.predict.Final.glmnet.Y
## sprice.d20nexp                         2.681004e-14
## [1] "newobs sold.fctr.predict.Final.glmnet N: max > max of Train range: 1"
##      UniqueID sold.fctr.predict.Final.glmnet D.wrds.stop.n.log
## 2614    12625                              N          2.397895
##      sprice.log10 sprice.root2
## 2614     6.907745     31.62262
##                                  id        cor.y exclude.as.feat
## D.wrds.stop.n.log D.wrds.stop.n.log  0.003851183           FALSE
## sprice.log10           sprice.log10 -0.469398937           FALSE
## sprice.root2           sprice.root2 -0.511275385           FALSE
##                     cor.y.abs   cor.high.X freqRatio percentUnique zeroVar
## D.wrds.stop.n.log 0.003851183         <NA>  8.732484     0.5405405   FALSE
## sprice.log10      0.469398937 sprice.root2  2.807692    30.2162162   FALSE
## sprice.root2      0.511275385         <NA>  2.807692    30.2162162   FALSE
##                     nzv is.cor.y.abs.low interaction.feat
## D.wrds.stop.n.log FALSE            FALSE             <NA>
## sprice.log10      FALSE            FALSE             <NA>
## sprice.root2      FALSE            FALSE             <NA>
##                   shapiro.test.p.value rsp_var_raw id_var rsp_var
## D.wrds.stop.n.log         4.444633e-54       FALSE     NA      NA
## sprice.log10              1.697609e-47       FALSE     NA      NA
## sprice.root2              6.339275e-16       FALSE     NA      NA
##                         max      min max.sold.fctr.N max.sold.fctr.Y
## D.wrds.stop.n.log  2.564949  0.00000        2.302585        2.302585
## sprice.log10       6.907745 -4.60517        6.906755        6.514713
## sprice.root2      31.622618  0.10000       31.606961       25.980762
##                   min.sold.fctr.N min.sold.fctr.Y
## D.wrds.stop.n.log      0.00000000         0.00000
## sprice.log10          -0.01005034        -4.60517
## sprice.root2           0.99498744         0.10000
##                   max.sold.fctr.predict.All.X.glmnet.N
## D.wrds.stop.n.log                             2.302585
## sprice.log10                                  6.721414
## sprice.root2                                 28.809547
##                   max.sold.fctr.predict.All.X.glmnet.Y
## D.wrds.stop.n.log                             2.302585
## sprice.log10                                  6.445720
## sprice.root2                                 25.099801
##                   min.sold.fctr.predict.All.X.glmnet.N
## D.wrds.stop.n.log                             0.000000
## sprice.log10                                  1.095273
## sprice.root2                                  1.729162
##                   min.sold.fctr.predict.All.X.glmnet.Y
## D.wrds.stop.n.log                              0.00000
## sprice.log10                                  -4.60517
## sprice.root2                                   0.10000
##                   max.sold.fctr.predict.Final.glmnet.N
## D.wrds.stop.n.log                             2.397895
## sprice.log10                                  6.907745
## sprice.root2                                 31.622618
##                   max.sold.fctr.predict.Final.glmnet.Y
## D.wrds.stop.n.log                             2.564949
## sprice.log10                                  6.437752
## sprice.root2                                 25.000000
##                   min.sold.fctr.predict.Final.glmnet.N
## D.wrds.stop.n.log                             0.000000
## sprice.log10                                  1.731656
## sprice.root2                                  2.376973
##                   min.sold.fctr.predict.Final.glmnet.Y
## D.wrds.stop.n.log                              0.00000
## sprice.log10                                  -4.60517
## sprice.root2                                   0.10000
## [1] "newobs total range outliers: 5"
#stop(here"); glb_to_sav(); sav_obsout_df <- obsout_df; all.equal(sav_obsout_df, obsout_df); obsout_df <- sav_obsout_df

if (!is.null(glbOutStackFnames)) {
    for (fname in glbOutStackFnames) {
        print(sprintf("Stacking file %s to prediction output...", fname))
        #obsout_df <- dplyr::arrange_(rbind(obsout_df, read.csv(fname)), "UniqueID")
        obsout_df <- dplyr::arrange_(rbind(obsout_df, 
                #read.csv(fname) %>% filter(!(UniqueID %in% obsout_df$UniqueID))),
            #read.csv(fname) %>% filter(!(UniqueID %in% obsout_df[, glb_id_var]))),
            read.csv(fname) %>% 
                dplyr::filter_(interp(~!(var %in% obsout_df$var), 
                                      var = as.name(glb_id_var)))),
                                    glb_id_var)
        
        if (nrow(obsout_df) != length(unique(obsout_df[, glb_id_var])))
            stop("Potential dups in stacked prediction output")
    }
}

out_fname <- paste0(glb_out_pfx, "out.csv")
write.csv(obsout_df, out_fname, quote = FALSE, row.names = FALSE)
#cat(" ", "\n", file=submit_fn, append=TRUE)

# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("id", 
#             "max.auc.OOB", "max.Accuracy.OOB")]))
for (txt_var in glbFeatsText) {
    # Print post-stem-words but need post-stop-words for debugging ?
    print(sprintf("    All post-stem-words term weights for %s:", txt_var))
    myprint_df(glb_post_stem_words_terms_df_lst[[txt_var]])
    terms_mtrx <- glb_post_stem_words_terms_mtrx_lst[[txt_var]]
    print(glb_allobs_df[
        which(terms_mtrx[, tail(glb_post_stem_words_terms_df_lst[[txt_var]], 1)$pos] > 0), 
                        c(glb_id_var, glbFeatsText)])
    print(nrow(subset(glb_post_stem_words_terms_df_lst[[txt_var]], freq == 1)))
    #print(glb_allobs_df[which(terms_mtrx[, 207] > 0), c(glb_id_var, glbFeatsText)])
    #unlist(strsplit(glb_allobs_df[2157, "description"], ""))
    #glb_allobs_df[2442, c(glb_id_var, glbFeatsText)]
    #terms_mtrx[2442, terms_mtrx[2442, ] > 0]  
    
    print(sprintf("    All post-stem-words term freq distribution for %s:", txt_var))
    print(table(glb_post_stem_words_terms_df_lst[[txt_var]]$freq))
    print(sprintf("    All post-stem-words term length distribution for %s:", txt_var))
    print(table(nchar(glb_post_stem_words_terms_df_lst[[txt_var]]$term)))
    print(subset(glb_post_stem_words_terms_df_lst[[txt_var]], nchar(term) >= 10))

    print(sprintf("    Analyzed term weights for %s:", txt_var))
    tmp_df <- glb_post_stem_words_terms_df_lst[[txt_var]]
    anl_terms_vctr <- union(select_terms, assoc_terms)
    print(subset(tmp_df, term %in% anl_terms_vctr))
#     tmp_freq1_df <- subset(tmp_df, freq == 1)
#     tmp_freq1_df$top_n <- grepl(paste0(top_n_vctr, collapse="|"), tmp_freq1_df$term)
#     print(subset(tmp_freq1_df, top_n == TRUE))
}
## [1] "    All post-stem-words term weights for descr.my:"
##            term    weight freq pos        cor.y   cor.y.abs chisq.stat
## condit   condit 160.55353  492  52 -0.033248924 0.033248924   68.76178
## likenew likenew 123.78010   70 117 -0.043168766 0.043168766   22.07653
## use         use 111.06226  285 210  0.003025811 0.003025811   45.28121
## in           in 102.43546  432  98 -0.071653919 0.071653919   40.30764
## and         and  92.37461  331  20  0.006010357 0.006010357   38.45444
## is           is  87.72451  335 110 -0.042931491 0.042931491   26.34374
##           chisq.pval nzv.freqRatio nzv.percentUnique  nzv weight.N
## condit  3.361980e-06      36.82927          1.351351 TRUE 61.51671
## likenew 1.817939e-01     257.71429          0.972973 TRUE 54.11495
## use     1.518411e-02      86.63158          1.513514 TRUE 40.77891
## in      1.981535e-02      33.52174          1.351351 TRUE 46.12932
## and     7.095553e-02      55.24138          1.513514 TRUE 35.73653
## is      5.541564e-01      54.06667          1.567568 TRUE 35.26992
##         weight.Y weight.NA
## condit  43.14002  55.89681
## likenew 19.52427  50.14088
## use     35.93377  34.34958
## in      26.82731  29.47884
## and     31.78712  24.85096
## is      23.45595  28.99864
##              term    weight freq pos        cor.y   cor.y.abs chisq.stat
## has           has 59.117295  176  91  0.041309583 0.041309583  19.172726
## day           day 16.506195   31  59 -0.031464000 0.031464000  10.200442
## owner       owner 11.215175   14 151 -0.010637108 0.010637108   5.718283
## ipadair2 ipadair2  8.168607    9 106  0.017806052 0.017806052   4.057710
## can           can  7.358665   12  43  0.009697873 0.009697873   6.069230
## wifion     wifion  4.580205    6 221 -0.035036304 0.035036304   2.582077
##          chisq.pval nzv.freqRatio nzv.percentUnique  nzv  weight.N
## has       0.5740608        86.300         1.1891892 TRUE 18.221776
## day       0.2512385       228.125         0.4864865 TRUE  8.657983
## owner     0.2211980       614.000         0.2702703 TRUE  4.254375
## ipadair2  0.5411374       922.000         0.3243243 TRUE  2.196428
## can       0.4154798      1844.000         0.3783784 TRUE  1.550491
## wifion    0.4606404      1847.000         0.2162162 TRUE  2.164131
##           weight.Y weight.NA
## has      21.925360 18.970159
## day       4.166689  3.681523
## owner     2.417015  4.543785
## ipadair2  3.820810  2.151369
## can       1.885367  3.922808
## wifion    0.000000  2.416074
##            term   weight freq pos         cor.y    cor.y.abs   chisq.stat
## 4g           4g 2.200200    3   9  0.0002965679 0.0002965679 2.023052e+00
## sprint   sprint 2.037099    2 191 -0.0215577316 0.0215577316 1.281078e-27
## verizon verizon 2.037099    2 212 -0.0215577316 0.0215577316 1.281078e-27
## tmobil   tmobil 1.893062    2 203  0.0094480109 0.0094480109 2.023052e+00
## ipad1     ipad1 1.834814    2 101  0.0067428569 0.0067428569 2.023052e+00
## gold       gold 1.137069    1  86 -0.0215577316 0.0215577316 1.281078e-27
##         chisq.pval nzv.freqRatio nzv.percentUnique  nzv  weight.N
## 4g       0.3636637          1848         0.1621622 TRUE 0.6989804
## sprint   1.0000000          1849         0.1081081 TRUE 0.7407634
## verizon  1.0000000          1849         0.1081081 TRUE 0.7407634
## tmobil   0.3636637          1848         0.1621622 TRUE 0.7407634
## ipad1    0.3636637          1848         0.1621622 TRUE 0.7977452
## gold     1.0000000          1849         0.1081081 TRUE 1.1370687
##          weight.Y weight.NA
## 4g      0.6116078 0.8896114
## sprint  0.0000000 1.2963359
## verizon 0.0000000 1.2963359
## tmobil  1.1522986 0.0000000
## ipad1   1.0370687 0.0000000
## gold    0.0000000 0.0000000
##      UniqueID
## 1177    11183
##                                                                                descr.my
## 1177 *** Likenew, Apple iPadAir2 Gold 128GB. Comes with AC adapter and lightning cable.
## [1] 1
## [1] "    All post-stem-words term freq distribution for descr.my:"
## 
##   1   2   3   4   5   6   8   9  10  11  12  13  14  15  16  17  18  19 
##   1   4   3   3   1   2   1   1  13   8  13   6  13   5   9   5   6   8 
##  20  21  22  23  24  25  26  27  28  30  31  32  34  35  36  37  38  40 
##   2   5   1   3   1   2   5   8   3   1   7   2   3   1   1   3   2   2 
##  42  43  44  45  46  50  51  52  53  54  55  57  58  59  61  63  64  65 
##   1   2   1   2   2   1   1   2   1   1   1   2   3   1   1   1   1   1 
##  67  70  72  75  77  78  79  80  81  82  88  97  99 100 101 106 109 117 
##   1   1   2   2   3   1   1   1   1   2   1   1   1   1   1   1   2   2 
## 125 129 151 154 160 167 176 197 198 208 211 214 249 279 285 286 331 335 
##   2   1   1   1   1   1   2   1   1   1   1   1   1   1   1   1   1   1 
## 432 492 
##   1   1 
## [1] "    All post-stem-words term length distribution for descr.my:"
## 
##  1  2  3  4  5  6  7  8  9 10 
##  6 17 28 64 51 29 15  8  6  2 
##                  term   weight freq pos       cor.y  cor.y.abs chisq.stat
## profession profession 9.495620   16 160 -0.07142724 0.07142724   9.508800
## manufactur manufactur 8.616795    8 123  0.01332077 0.01332077   6.928535
##            chisq.pval nzv.freqRatio nzv.percentUnique  nzv weight.N
## profession 0.04956674         613.0         0.2702703 TRUE 6.606743
## manufactur 0.32750691         921.5         0.3783784 TRUE 3.089182
##            weight.Y weight.NA
## profession 0.000000  2.888877
## manufactur 4.481277  1.046336
## [1] "    Analyzed term weights for descr.my:"
##          term    weight freq pos        cor.y   cor.y.abs chisq.stat
## condit condit 160.55353  492  52 -0.033248924 0.033248924   68.76178
## use       use 111.06226  285 210  0.003025811 0.003025811   45.28121
## in         in 102.43546  432  98 -0.071653919 0.071653919   40.30764
## good     good  86.95851  197  87  0.016394675 0.016394675   33.86257
## screen screen  73.07057  208 172  0.040605274 0.040605274   32.79902
## with     with  63.69164  214 223 -0.066087601 0.066087601   33.25670
## of         of  52.99927  151 139  0.052259530 0.052259530   34.49697
## mint     mint  52.07871   63 128 -0.055691207 0.055691207   32.30119
## or         or  51.11272  125 144 -0.022614420 0.022614420   36.49790
## cosmet cosmet  43.18436  117  55 -0.088480863 0.088480863   32.40489
## minor   minor  41.27081  117 127 -0.042692360 0.042692360   33.12974
## light   light  36.41889   81 116 -0.032814746 0.032814746   35.12786
## 100       100  27.52852   64   3 -0.111398502 0.111398502   30.45739
## from     from  24.73125   57  78 -0.030653359 0.030653359   34.28905
## hous     hous  24.59165   72  94 -0.129274878 0.129274878   41.95872
##          chisq.pval nzv.freqRatio nzv.percentUnique  nzv weight.N
## condit 3.361980e-06      36.82927         1.3513514 TRUE 61.51671
## use    1.518411e-02      86.63158         1.5135135 TRUE 40.77891
## in     1.981535e-02      33.52174         1.3513514 TRUE 46.12932
## good   3.748545e-02      95.16667         1.1891892 TRUE 29.35392
## screen 3.549380e-02      70.91667         1.1351351 TRUE 23.12678
## with   1.553541e-02      46.88889         1.0270270 TRUE 30.89756
## of     2.295297e-02     145.00000         1.1351351 TRUE 16.02233
## mint   2.024848e-02      94.89474         1.0270270 TRUE 24.27170
## or     9.163373e-03      87.95000         1.0810811 TRUE 22.27352
## cosmet 3.507470e-03      88.40000         0.8108108 TRUE 22.73428
## minor  1.085239e-02      92.52632         0.9729730 TRUE 20.58455
## light  3.817238e-03      99.27778         0.9189189 TRUE 17.42219
## 100    1.342479e-03     180.50000         0.6486486 TRUE 18.16396
## from   6.072414e-04      95.31579         0.7027027 TRUE 10.97996
## hous   3.344151e-06      81.68182         0.5405405 TRUE 17.25642
##         weight.Y weight.NA
## condit 43.140019 55.896806
## use    35.933771 34.349578
## in     26.827307 29.478838
## good   30.064015 27.540573
## screen 28.028461 21.915331
## with   16.195788 16.598293
## of     21.710580 15.266363
## mint    6.994811 20.812208
## or     15.175342 13.663856
## cosmet  7.055599 13.394481
## minor  11.627271  9.058990
## light  10.171779  8.824924
## 100     1.314682  8.049871
## from    5.861552  7.889737
## hous    1.193859  6.141364
if (glb_is_classification && glb_is_binomial)
    print(glb_models_df[glb_models_df$id == glb_sel_mdl_id, 
                        "opt.prob.threshold.OOB"])
## [1] 0.3
print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))
## [1] "glb_sel_mdl_id: All.X.glmnet"
print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))
## [1] "glb_fin_mdl_id: Final.glmnet"
get_dsp_models_df()
## [1] "Cross Validation issues:"
## Warning in get_dsp_models_df(): Cross Validation issues:
##            MFO.myMFO_classfr      Random.myrandom_classfr 
##                            0                            0 
##     Max.cor.Y.rcv.1X1.glmnet Max.cor.Y.rcv.1X1.cp.0.rpart 
##                            0                            0
##                                                                  id
## RFE.X.glm                                                 RFE.X.glm
## Max.cor.Y.rcv.1X1.glmnet                   Max.cor.Y.rcv.1X1.glmnet
## Interact.High.cor.Y.glmnet               Interact.High.cor.Y.glmnet
## Max.cor.Y.rpart                                     Max.cor.Y.rpart
## Low.cor.X.glmnet                                   Low.cor.X.glmnet
## Max.cor.Y.rcv.3X3.glmnet                   Max.cor.Y.rcv.3X3.glmnet
## Max.cor.Y.rcv.1X1.Interact.glmnet Max.cor.Y.rcv.1X1.Interact.glmnet
## Max.cor.Y.rcv.3X1.glmnet                   Max.cor.Y.rcv.3X1.glmnet
## Max.cor.Y.rcv.3X5.glmnet                   Max.cor.Y.rcv.3X5.glmnet
## Max.cor.Y.rcv.5X1.glmnet                   Max.cor.Y.rcv.5X1.glmnet
## Max.cor.Y.rcv.5X3.glmnet                   Max.cor.Y.rcv.5X3.glmnet
## Max.cor.Y.rcv.5X5.glmnet                   Max.cor.Y.rcv.5X5.glmnet
## RFE.X.glmnet                                           RFE.X.glmnet
## All.X.glmnet                                           All.X.glmnet
## Max.cor.Y.rcv.1X1.cp.0.rpart           Max.cor.Y.rcv.1X1.cp.0.rpart
## MFO.myMFO_classfr                                 MFO.myMFO_classfr
## Random.myrandom_classfr                     Random.myrandom_classfr
## Final.glmnet                                           Final.glmnet
## Final.glm                                                 Final.glm
##                                   max.Accuracy.OOB max.AUCROCR.OOB
## RFE.X.glm                                0.7879518       0.8700475
## Max.cor.Y.rcv.1X1.glmnet                 0.7722892       0.8197236
## Interact.High.cor.Y.glmnet               0.7698795       0.8189000
## Max.cor.Y.rpart                          0.7698795       0.7855854
## Low.cor.X.glmnet                         0.7686747       0.8727052
## Max.cor.Y.rcv.3X3.glmnet                 0.7662651       0.8197470
## Max.cor.Y.rcv.1X1.Interact.glmnet        0.7662651       0.8197470
## Max.cor.Y.rcv.3X1.glmnet                 0.7662651       0.8197353
## Max.cor.Y.rcv.3X5.glmnet                 0.7650602       0.8196593
## Max.cor.Y.rcv.5X1.glmnet                 0.7638554       0.8195600
## Max.cor.Y.rcv.5X3.glmnet                 0.7638554       0.8195600
## Max.cor.Y.rcv.5X5.glmnet                 0.7638554       0.8193790
## RFE.X.glmnet                             0.7614458       0.8721444
## All.X.glmnet                             0.7614458       0.8721444
## Max.cor.Y.rcv.1X1.cp.0.rpart             0.7433735       0.7934621
## MFO.myMFO_classfr                        0.4614458       0.5000000
## Random.myrandom_classfr                  0.4614458       0.4956046
## Final.glmnet                                    NA              NA
## Final.glm                                       NA              NA
##                                   max.AUCpROC.OOB max.Accuracy.fit
## RFE.X.glm                               0.7851882        0.8307135
## Max.cor.Y.rcv.1X1.glmnet                0.7665376        0.7843137
## Interact.High.cor.Y.glmnet              0.7551533        0.8026098
## Max.cor.Y.rpart                         0.7445254        0.7964073
## Low.cor.X.glmnet                        0.7801532        0.8349657
## Max.cor.Y.rcv.3X3.glmnet                0.7603840        0.7954221
## Max.cor.Y.rcv.1X1.Interact.glmnet       0.7603840        0.7954221
## Max.cor.Y.rcv.3X1.glmnet                0.7592654        0.7950911
## Max.cor.Y.rcv.3X5.glmnet                0.7603840        0.7962655
## Max.cor.Y.rcv.5X1.glmnet                0.7615026        0.7990899
## Max.cor.Y.rcv.5X3.glmnet                0.7615026        0.7964364
## Max.cor.Y.rcv.5X5.glmnet                0.7641135        0.7970687
## RFE.X.glmnet                            0.7835089        0.8303886
## All.X.glmnet                            0.7835089        0.8303886
## Max.cor.Y.rcv.1X1.cp.0.rpart            0.7504950        0.8323529
## MFO.myMFO_classfr                       0.5000000        0.4627451
## Random.myrandom_classfr                 0.5064252        0.4627451
## Final.glmnet                                   NA        0.8203656
## Final.glm                                      NA        0.8185584
##                                   opt.prob.threshold.fit
## RFE.X.glm                                            0.4
## Max.cor.Y.rcv.1X1.glmnet                             0.4
## Interact.High.cor.Y.glmnet                           0.4
## Max.cor.Y.rpart                                      0.5
## Low.cor.X.glmnet                                     0.5
## Max.cor.Y.rcv.3X3.glmnet                             0.5
## Max.cor.Y.rcv.1X1.Interact.glmnet                    0.5
## Max.cor.Y.rcv.3X1.glmnet                             0.5
## Max.cor.Y.rcv.3X5.glmnet                             0.5
## Max.cor.Y.rcv.5X1.glmnet                             0.5
## Max.cor.Y.rcv.5X3.glmnet                             0.5
## Max.cor.Y.rcv.5X5.glmnet                             0.5
## RFE.X.glmnet                                         0.4
## All.X.glmnet                                         0.4
## Max.cor.Y.rcv.1X1.cp.0.rpart                         0.4
## MFO.myMFO_classfr                                    0.4
## Random.myrandom_classfr                              0.4
## Final.glmnet                                         0.4
## Final.glm                                            0.4
##                                   opt.prob.threshold.OOB
## RFE.X.glm                                            0.5
## Max.cor.Y.rcv.1X1.glmnet                             0.4
## Interact.High.cor.Y.glmnet                           0.3
## Max.cor.Y.rpart                                      0.3
## Low.cor.X.glmnet                                     0.3
## Max.cor.Y.rcv.3X3.glmnet                             0.4
## Max.cor.Y.rcv.1X1.Interact.glmnet                    0.4
## Max.cor.Y.rcv.3X1.glmnet                             0.4
## Max.cor.Y.rcv.3X5.glmnet                             0.4
## Max.cor.Y.rcv.5X1.glmnet                             0.4
## Max.cor.Y.rcv.5X3.glmnet                             0.4
## Max.cor.Y.rcv.5X5.glmnet                             0.4
## RFE.X.glmnet                                         0.3
## All.X.glmnet                                         0.3
## Max.cor.Y.rcv.1X1.cp.0.rpart                         0.3
## MFO.myMFO_classfr                                    0.4
## Random.myrandom_classfr                              0.4
## Final.glmnet                                          NA
## Final.glm                                             NA
if (glb_is_regression) {
    print(sprintf("%s OOB RMSE: %0.4f", glb_sel_mdl_id,
            glb_models_df[glb_models_df$id == glb_sel_mdl_id, "min.RMSE.OOB"]))

    if (!is.null(glb_category_var)) {
#stop(here"); glb_to_sav(); glb_ctgry_df <- sav_ctgry_df        

#         OOB_ctgry_df <- myget_category_stats(glb_OOBobs_df, glb_sel_mdl_id, "OOB")
#         glb_ctgry_df <- merge(glb_ctgry_df, subset(OOB_ctgry_df, select=-.n.OOB),
#                               by=glb_category_var, all=TRUE)
#         
#         #glb_fitobs_df <- glb_get_predictions(glb_fitobs_df, glb_sel_mdl_id, glb_rsp_var_out)
#         glb_ctgry_df <- merge(glb_ctgry_df, 
#             myget_category_stats(obs_df=glb_fitobs_df, mdl_id=glb_sel_mdl_id, label="fit"),
#                               by=glb_category_var, all=TRUE)
#         row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
        glb_ctgry_df <- merge(glb_ctgry_df, 
            myget_category_stats(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id, label="trn"),
                              by=glb_category_var, all=TRUE)
        row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
        
        glb_ctgry_df <- merge(glb_ctgry_df, 
            myget_category_stats(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id, label="new"),
                              by=glb_category_var, all=TRUE)
        row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
        
        if (any(grepl("OOB", glbMdlMetricsEval)))
            print(orderBy(~-err.abs.OOB.mean, glb_ctgry_df)) else
            print(orderBy(~-err.abs.fit.mean, glb_ctgry_df))
        print(colSums(glb_ctgry_df[, -grep(glb_category_var, names(glb_ctgry_df))]))
    }
    
    if ((glb_rsp_var %in% names(glb_newobs_df)) &&
        !(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
            pred_stats_df <- 
                mypredict_mdl(mdl=glb_models_lst[[glb_fin_mdl_id]], 
                              df=glb_newobs_df, 
                              rsp_var=glb_rsp_var, 
                              rsp_var_out=glb_rsp_var_out, 
                              mdl_id=glb_fin_mdl_id, 
                              label="new",
                              model_summaryFunction=glb_sel_mdl$control$summaryFunction, 
                              model_metric=glb_sel_mdl$metric,
                              model_metric_maximize=glb_sel_mdl$maximize,
                              ret_type="stats")        
            print(sprintf("%s prediction stats for glb_newobs_df:", glb_fin_mdl_id))
            print(pred_stats_df)
    }    
}

if (glb_is_classification) {
    print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))
    print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)], 
                            glb_OOBobs_df[, glb_rsp_var])$table))

    if (!is.null(glb_category_var)) {
        glb_ctgry_df <- merge(glb_ctgry_df, 
            myget_category_stats(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id, label="trn"),
                              by=glb_category_var, all=TRUE)
        row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
        
        glb_ctgry_df <- merge(glb_ctgry_df, 
            myget_category_stats(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id, label="new"),
                              by=glb_category_var, all=TRUE)
        row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
        
        if (any(grepl("OOB", glbMdlMetricsEval)))
            print(orderBy(~-err.abs.OOB.mean, glb_ctgry_df[, -1])) else
            print(orderBy(~-err.abs.fit.mean, glb_ctgry_df[, -1]))
        print(colSums(glb_ctgry_df[, -grep(glb_category_var, names(glb_ctgry_df))]))
        
#         print("Top category OOB errors:")
#         print(glb_OOBobs_df[(glb_OOBobs_df[, glb_category_var] == 
#                                 dsp_ctgry_df[1, glb_category_var]) & 
#                             !glb_OOBobs_df[, predct_accurate_var_name], 
#             c(glb_id_var, glb_rsp_var_raw, paste0(glb_rsp_var_out, glb_sel_mdl_id),
#               glb_category_var,
#                           row.names(head(myget_feats_importance(glb_sel_mdl), 5)),
#                               # "biddable", "startprice", "condition",
#                           glbFeatsText)])
    }
    
    if ((glb_rsp_var %in% names(glb_newobs_df)) &&
        !(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
        print(sprintf("%s new confusion matrix & accuracy: ", glb_fin_mdl_id))
        print(t(confusionMatrix(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)], 
                                glb_newobs_df[, glb_rsp_var])$table))
    }    
}    
## [1] "All.X.glmnet OOB confusion matrix & accuracy: "
##          Prediction
## Reference   N   Y
##         N 309 138
##         Y  60 323
##           .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## iPad1         95    130     88     0.12745098     0.11445783
## iPadmini2     53     54     56     0.05294118     0.06385542
## Unknown       96    108     92     0.10588235     0.11566265
## iPadmini     123    154    111     0.15098039     0.14819277
## iPadAir2      69    102     62     0.10000000     0.08313253
## iPad2        142    144    154     0.14117647     0.17108434
## iPad3         61     92     55     0.09019608     0.07349398
## iPadmini3     36     54     38     0.05294118     0.04337349
## iPadAir       82     98     74     0.09607843     0.09879518
## iPad4         73     84     68     0.08235294     0.08795181
##           .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit
## iPad1         0.11027569        23.91786        0.1839835    130
## iPadmini2     0.07017544        14.19476        0.2628659     54
## Unknown       0.11528822        26.52091        0.2455640    108
## iPadmini      0.13909774        30.21777        0.1962193    154
## iPadAir2      0.07769424        21.24831        0.2083168    102
## iPad2         0.19298246        31.86069        0.2212548    144
## iPad3         0.06892231        21.85916        0.2375996     92
## iPadmini3     0.04761905        10.64581        0.1971447     54
## iPadAir       0.09273183        13.80524        0.1408698     98
## iPad4         0.08521303        10.53643        0.1254337     84
##           err.abs.OOB.sum err.abs.OOB.mean err.abs.trn.sum
## iPad1           31.738295        0.3340873        59.73383
## iPadmini2       15.149743        0.2858442        32.44337
## Unknown         25.545260        0.2660965        55.98075
## iPadmini        32.704594        0.2658910        75.52109
## iPadAir2        17.785002        0.2577536        44.49984
## iPad2           35.143654        0.2474905        74.79123
## iPad3           14.689955        0.2408189        39.63516
## iPadmini3        8.566058        0.2379461        21.58114
## iPadAir         15.249371        0.1859679        36.67291
## iPad4           13.177556        0.1805145        33.00200
##           err.abs.trn.mean .n.trn err.abs.new.sum err.abs.new.mean .n.new
## iPad1            0.2654837    225              NA               NA     88
## iPadmini2        0.3032091    107              NA               NA     56
## Unknown          0.2744154    204              NA               NA     92
## iPadmini         0.2726393    277              NA               NA    111
## iPadAir2         0.2602330    171              NA               NA     62
## iPad2            0.2615078    286              NA               NA    154
## iPad3            0.2590533    153              NA               NA     55
## iPadmini3        0.2397904     90              NA               NA     38
## iPadAir          0.2037384    180              NA               NA     74
## iPad4            0.2102038    157              NA               NA     68
##           .n.OOB           .n.Fit           .n.Tst   .freqRatio.Fit 
##       830.000000      1020.000000       798.000000         1.000000 
##   .freqRatio.OOB   .freqRatio.Tst  err.abs.fit.sum err.abs.fit.mean 
##         1.000000         1.000000       204.806955         2.019252 
##           .n.fit  err.abs.OOB.sum err.abs.OOB.mean  err.abs.trn.sum 
##      1020.000000       209.749487         2.502411       473.861322 
## err.abs.trn.mean           .n.trn  err.abs.new.sum err.abs.new.mean 
##         2.550274      1850.000000               NA               NA 
##           .n.new 
##       798.000000
if (!is.null(glb_featsimp_df))
    print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")), 
                  subset(glb_featsimp_df, importance > 10)))
##                                           All.X.glmnet.importance
## spdiff.cut.fctr(-1,0]                                   100.00000
## spdiff.cut.fctr(1,10]                                    91.77825
## spdiff.cut.fctr(0,1]                                     91.77769
## spdiff.cut.fctr(-10,-1]                                  89.53891
## spdiff.cut.fctr(10,100]                                  86.86797
## cellular.fctr1:carrier.fctrOther                         75.82223
## biddable                                                 75.17563
## spdiff.cut.fctr(-100,-10]                                73.61063
## prdl.my.fctriPadAir2                                     69.61045
## storage.fctrUnknown                                      68.07849
## prdl.my.fctriPadAir                                      66.43833
## prdl.my.fctriPad4                                        65.16814
## prdl.my.fctriPadmini2                                    64.52272
## D.wrds.n.log                                             63.88632
## spdiff.cut.fctr(100,1e+03]                               62.38931
## cellular.fctr1:carrier.fctrSprint                        61.81532
## prdl.my.fctrUnknown:.clusterid.fctr2                     60.62908
## prdl.my.fctriPadmini3                                    59.42417
## condition.fctrNew other (see details)                    59.12005
## D.chrs.pnct11.n.log                                      56.89033
## color.fctrUnknown                                        55.86778
## cellular.fctr1:carrier.fctrVerizon                       55.50911
## prdl.my.fctriPad3:.clusterid.fctr2                       55.10589
## storage.fctr64                                           54.64454
## prdl.my.fctriPad3                                        54.34045
## cellular.fctr0:carrier.fctrNone                          54.22434
## startprice.dgt2.is9                                      53.73722
## D.wrds.stop.n.log                                        53.73265
## color.fctrGold                                           53.48381
## condition.fctrNew                                        52.97506
## startprice.dgt1.is9                                      52.93673
## storage.fctr32                                           52.71638
## cellular.fctr1                                           52.71142
## D.weight.post.stop.sum                                   52.71142
## cellular.fctr0:carrier.fctrOther                         52.71142
## cellular.fctr0:carrier.fctrSprint                        52.71142
## cellular.fctr0:carrier.fctrT-Mobile                      52.71142
## cellular.fctr0:carrier.fctrUnknown                       52.71142
## cellular.fctr0:carrier.fctrVerizon                       52.71142
## cellular.fctr1:carrier.fctrNone                          52.71142
## cellular.fctrUnknown:carrier.fctrNone                    52.71142
## cellular.fctrUnknown:carrier.fctrOther                   52.71142
## cellular.fctrUnknown:carrier.fctrSprint                  52.71142
## cellular.fctrUnknown:carrier.fctrT-Mobile                52.71142
## cellular.fctrUnknown:carrier.fctrVerizon                 52.71142
## prdl.my.fctriPad2:.clusterid.fctr2                       52.71142
## prdl.my.fctriPad2:.clusterid.fctr3                       52.71142
## prdl.my.fctriPad3:.clusterid.fctr3                       52.71142
## prdl.my.fctriPadAir2:.clusterid.fctr2                    52.71142
## prdl.my.fctriPadAir2:.clusterid.fctr3                    52.71142
## prdl.my.fctriPadAir:.clusterid.fctr3                     52.71142
## prdl.my.fctriPadmini2:.clusterid.fctr3                   52.71142
## prdl.my.fctriPadmini3:.clusterid.fctr2                   52.71142
## prdl.my.fctriPadmini3:.clusterid.fctr3                   52.71142
## prdl.my.fctriPadmini:.clusterid.fctr2                    52.71142
## prdl.my.fctriPadmini:.clusterid.fctr3                    52.71142
## D.chrs.uppr.n.log                                        52.60858
## cellular.fctr1:carrier.fctrUnknown                       52.55260
## .rnorm                                                   52.38568
## D.ratio.weight.sum.wrds.n                                52.18561
## startprice.dcm2.is9                                      51.96783
## sprice.root2                                             51.74950
## D.weight.sum                                             51.66462
## D.weight.post.stem.sum                                   51.63919
## storage.fctr16                                           51.55882
## D.chrs.pnct13.n.log                                      51.24550
## D.wrds.unq.n.log                                         51.14189
## color.fctrWhite                                          50.71378
## cellular.fctr1:carrier.fctrT-Mobile                      50.21020
## D.chrs.n.log                                             50.18255
## D.terms.post.stem.n.log                                  50.17209
## prdl.my.fctriPadAir:.clusterid.fctr2                     49.80426
## color.fctrSpace Gray                                     49.63204
## prdl.my.fctriPad2                                        48.61429
## prdl.my.fctriPadmini                                     48.49638
## startprice.dcm1.is9                                      48.37251
## sprice.log10                                             47.59704
## prdl.my.fctriPadmini2:.clusterid.fctr2                   47.13793
## prdl.my.fctriPad1                                        46.63375
## condition.fctrFor parts or not working                   46.18220
## prdl.my.fctriPad1:.clusterid.fctr2                       46.14804
## prdl.my.fctrUnknown:.clusterid.fctr3                     45.97618
## cellular.fctrUnknown:carrier.fctrUnknown                 45.72360
## cellular.fctrUnknown                                     45.68888
## D.terms.post.stop.n.log                                  44.49862
## condition.fctrSeller refurbished                         43.60662
## prdl.my.fctriPad1:.clusterid.fctr3                       42.66158
## condition.fctrManufacturer refurbished                   42.10131
## prdl.my.fctriPad4:.clusterid.fctr2                       39.81232
## sprice.d20nexp                                           38.83227
## D.weight.sum.stem.stop.Ratio                             26.99368
## D.ratio.wrds.stop.n.wrds.n                               26.94246
##                                           importance
## spdiff.cut.fctr(-1,0]                      100.00000
## spdiff.cut.fctr(1,10]                       80.40773
## spdiff.cut.fctr(0,1]                        85.31015
## spdiff.cut.fctr(-10,-1]                     84.15852
## spdiff.cut.fctr(10,100]                     72.75819
## cellular.fctr1:carrier.fctrOther            38.39431
## biddable                                    71.47982
## spdiff.cut.fctr(-100,-10]                   57.67475
## prdl.my.fctriPadAir2                        53.70014
## storage.fctrUnknown                         38.39431
## prdl.my.fctriPadAir                         48.86583
## prdl.my.fctriPad4                           41.79089
## prdl.my.fctriPadmini2                       41.25744
## D.wrds.n.log                                38.39431
## spdiff.cut.fctr(100,1e+03]                  42.57950
## cellular.fctr1:carrier.fctrSprint           41.47670
## prdl.my.fctrUnknown:.clusterid.fctr2        45.06580
## prdl.my.fctriPadmini3                       38.39431
## condition.fctrNew other (see details)       45.89626
## D.chrs.pnct11.n.log                         38.38991
## color.fctrUnknown                           38.39431
## cellular.fctr1:carrier.fctrVerizon          38.39431
## prdl.my.fctriPad3:.clusterid.fctr2          34.83380
## storage.fctr64                              42.47779
## prdl.my.fctriPad3                           38.39431
## cellular.fctr0:carrier.fctrNone             38.39431
## startprice.dgt2.is9                         38.39431
## D.wrds.stop.n.log                           38.39431
## color.fctrGold                              38.39431
## condition.fctrNew                           38.39431
## startprice.dgt1.is9                         38.39431
## storage.fctr32                              38.33297
## cellular.fctr1                              39.59945
## D.weight.post.stop.sum                      38.39431
## cellular.fctr0:carrier.fctrOther            38.39431
## cellular.fctr0:carrier.fctrSprint           38.39431
## cellular.fctr0:carrier.fctrT-Mobile         38.39431
## cellular.fctr0:carrier.fctrUnknown          38.39431
## cellular.fctr0:carrier.fctrVerizon          38.39431
## cellular.fctr1:carrier.fctrNone             38.39431
## cellular.fctrUnknown:carrier.fctrNone       38.39431
## cellular.fctrUnknown:carrier.fctrOther      38.39431
## cellular.fctrUnknown:carrier.fctrSprint     38.39431
## cellular.fctrUnknown:carrier.fctrT-Mobile   38.39431
## cellular.fctrUnknown:carrier.fctrVerizon    38.39431
## prdl.my.fctriPad2:.clusterid.fctr2          38.39431
## prdl.my.fctriPad2:.clusterid.fctr3          38.39431
## prdl.my.fctriPad3:.clusterid.fctr3          38.39431
## prdl.my.fctriPadAir2:.clusterid.fctr2       38.39431
## prdl.my.fctriPadAir2:.clusterid.fctr3       38.39431
## prdl.my.fctriPadAir:.clusterid.fctr3        38.39431
## prdl.my.fctriPadmini2:.clusterid.fctr3      38.39431
## prdl.my.fctriPadmini3:.clusterid.fctr2      38.39431
## prdl.my.fctriPadmini3:.clusterid.fctr3      38.39431
## prdl.my.fctriPadmini:.clusterid.fctr2       38.39431
## prdl.my.fctriPadmini:.clusterid.fctr3       38.39431
## D.chrs.uppr.n.log                           38.39431
## cellular.fctr1:carrier.fctrUnknown          38.39431
## .rnorm                                      38.39431
## D.ratio.weight.sum.wrds.n                   38.38146
## startprice.dcm2.is9                         38.39431
## sprice.root2                                36.91983
## D.weight.sum                                38.39431
## D.weight.post.stem.sum                      38.39431
## storage.fctr16                              38.39431
## D.chrs.pnct13.n.log                         38.11481
## D.wrds.unq.n.log                            38.39431
## color.fctrWhite                             37.93000
## cellular.fctr1:carrier.fctrT-Mobile         39.35301
## D.chrs.n.log                                38.39431
## D.terms.post.stem.n.log                     38.39431
## prdl.my.fctriPadAir:.clusterid.fctr2        38.39431
## color.fctrSpace Gray                        38.39431
## prdl.my.fctriPad2                           32.84184
## prdl.my.fctriPadmini                        35.61259
## startprice.dcm1.is9                         33.50057
## sprice.log10                                35.80737
## prdl.my.fctriPadmini2:.clusterid.fctr2      38.39431
## prdl.my.fctriPad1                           30.61657
## condition.fctrFor parts or not working      34.63052
## prdl.my.fctriPad1:.clusterid.fctr2          31.35476
## prdl.my.fctrUnknown:.clusterid.fctr3        38.39379
## cellular.fctrUnknown:carrier.fctrUnknown    38.25950
## cellular.fctrUnknown                        36.13550
## D.terms.post.stop.n.log                     38.24626
## condition.fctrSeller refurbished            31.20851
## prdl.my.fctriPad1:.clusterid.fctr3          25.94724
## condition.fctrManufacturer refurbished      38.39431
## prdl.my.fctriPad4:.clusterid.fctr2          38.39431
## sprice.d20nexp                              38.39431
## D.weight.sum.stem.stop.Ratio                38.26292
## D.ratio.wrds.stop.n.wrds.n                  38.39431
##                                           Final.glmnet.importance
## spdiff.cut.fctr(-1,0]                                   100.00000
## spdiff.cut.fctr(1,10]                                    80.40773
## spdiff.cut.fctr(0,1]                                     85.31015
## spdiff.cut.fctr(-10,-1]                                  84.15852
## spdiff.cut.fctr(10,100]                                  72.75819
## cellular.fctr1:carrier.fctrOther                         38.39431
## biddable                                                 71.47982
## spdiff.cut.fctr(-100,-10]                                57.67475
## prdl.my.fctriPadAir2                                     53.70014
## storage.fctrUnknown                                      38.39431
## prdl.my.fctriPadAir                                      48.86583
## prdl.my.fctriPad4                                        41.79089
## prdl.my.fctriPadmini2                                    41.25744
## D.wrds.n.log                                             38.39431
## spdiff.cut.fctr(100,1e+03]                               42.57950
## cellular.fctr1:carrier.fctrSprint                        41.47670
## prdl.my.fctrUnknown:.clusterid.fctr2                     45.06580
## prdl.my.fctriPadmini3                                    38.39431
## condition.fctrNew other (see details)                    45.89626
## D.chrs.pnct11.n.log                                      38.38991
## color.fctrUnknown                                        38.39431
## cellular.fctr1:carrier.fctrVerizon                       38.39431
## prdl.my.fctriPad3:.clusterid.fctr2                       34.83380
## storage.fctr64                                           42.47779
## prdl.my.fctriPad3                                        38.39431
## cellular.fctr0:carrier.fctrNone                          38.39431
## startprice.dgt2.is9                                      38.39431
## D.wrds.stop.n.log                                        38.39431
## color.fctrGold                                           38.39431
## condition.fctrNew                                        38.39431
## startprice.dgt1.is9                                      38.39431
## storage.fctr32                                           38.33297
## cellular.fctr1                                           39.59945
## D.weight.post.stop.sum                                   38.39431
## cellular.fctr0:carrier.fctrOther                         38.39431
## cellular.fctr0:carrier.fctrSprint                        38.39431
## cellular.fctr0:carrier.fctrT-Mobile                      38.39431
## cellular.fctr0:carrier.fctrUnknown                       38.39431
## cellular.fctr0:carrier.fctrVerizon                       38.39431
## cellular.fctr1:carrier.fctrNone                          38.39431
## cellular.fctrUnknown:carrier.fctrNone                    38.39431
## cellular.fctrUnknown:carrier.fctrOther                   38.39431
## cellular.fctrUnknown:carrier.fctrSprint                  38.39431
## cellular.fctrUnknown:carrier.fctrT-Mobile                38.39431
## cellular.fctrUnknown:carrier.fctrVerizon                 38.39431
## prdl.my.fctriPad2:.clusterid.fctr2                       38.39431
## prdl.my.fctriPad2:.clusterid.fctr3                       38.39431
## prdl.my.fctriPad3:.clusterid.fctr3                       38.39431
## prdl.my.fctriPadAir2:.clusterid.fctr2                    38.39431
## prdl.my.fctriPadAir2:.clusterid.fctr3                    38.39431
## prdl.my.fctriPadAir:.clusterid.fctr3                     38.39431
## prdl.my.fctriPadmini2:.clusterid.fctr3                   38.39431
## prdl.my.fctriPadmini3:.clusterid.fctr2                   38.39431
## prdl.my.fctriPadmini3:.clusterid.fctr3                   38.39431
## prdl.my.fctriPadmini:.clusterid.fctr2                    38.39431
## prdl.my.fctriPadmini:.clusterid.fctr3                    38.39431
## D.chrs.uppr.n.log                                        38.39431
## cellular.fctr1:carrier.fctrUnknown                       38.39431
## .rnorm                                                   38.39431
## D.ratio.weight.sum.wrds.n                                38.38146
## startprice.dcm2.is9                                      38.39431
## sprice.root2                                             36.91983
## D.weight.sum                                             38.39431
## D.weight.post.stem.sum                                   38.39431
## storage.fctr16                                           38.39431
## D.chrs.pnct13.n.log                                      38.11481
## D.wrds.unq.n.log                                         38.39431
## color.fctrWhite                                          37.93000
## cellular.fctr1:carrier.fctrT-Mobile                      39.35301
## D.chrs.n.log                                             38.39431
## D.terms.post.stem.n.log                                  38.39431
## prdl.my.fctriPadAir:.clusterid.fctr2                     38.39431
## color.fctrSpace Gray                                     38.39431
## prdl.my.fctriPad2                                        32.84184
## prdl.my.fctriPadmini                                     35.61259
## startprice.dcm1.is9                                      33.50057
## sprice.log10                                             35.80737
## prdl.my.fctriPadmini2:.clusterid.fctr2                   38.39431
## prdl.my.fctriPad1                                        30.61657
## condition.fctrFor parts or not working                   34.63052
## prdl.my.fctriPad1:.clusterid.fctr2                       31.35476
## prdl.my.fctrUnknown:.clusterid.fctr3                     38.39379
## cellular.fctrUnknown:carrier.fctrUnknown                 38.25950
## cellular.fctrUnknown                                     36.13550
## D.terms.post.stop.n.log                                  38.24626
## condition.fctrSeller refurbished                         31.20851
## prdl.my.fctriPad1:.clusterid.fctr3                       25.94724
## condition.fctrManufacturer refurbished                   38.39431
## prdl.my.fctriPad4:.clusterid.fctr2                       38.39431
## sprice.d20nexp                                           38.39431
## D.weight.sum.stem.stop.Ratio                             38.26292
## D.ratio.wrds.stop.n.wrds.n                               38.39431
print("glb_newobs_df prediction stats:")
## [1] "glb_newobs_df prediction stats:"
if (glb_is_regression)
    print(myplot_histogram(glb_newobs_df, paste0(glb_rsp_var_out, glb_fin_mdl_id)))
if (glb_is_classification)
    print(table(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)]))
## 
##   N   Y 
## 345 453
# Use this to see how prediction changes by changing one or more values
# players_df <- data.frame(id=c("Chavez", "Giambi", "Menechino", "Myers", "Pena"),
#                          OBP=c(0.338, 0.391, 0.369, 0.313, 0.361),
#                          SLG=c(0.540, 0.450, 0.374, 0.447, 0.500),
#                         cost=c(1400000, 1065000, 295000, 800000, 300000))
# players_df$RS.predict <- predict(glb_models_lst[[csm_mdl_id]], players_df)
# print(orderBy(~ -RS.predict, players_df))
# dsp_chisq.test(Headline.contains="[Vi]deo")

if ((length(diff <- setdiff(names(glb_trnobs_df), names(glb_allobs_df))) > 0) ||
    (length(diff <- setdiff(names(glb_fitobs_df), names(glb_allobs_df))) > 0) ||
    (length(diff <- setdiff(names(glb_OOBobs_df), names(glb_allobs_df))) > 0) ||
    (length(diff <- setdiff(names(glb_newobs_df), names(glb_allobs_df))) > 0)) {
    print(diff)
    stop("glb_*obs_df not in sync")
}

if (glb_save_envir)
    save(glb_feats_df, glb_allobs_df, 
         #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
         glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
         glb_sel_mdl, glb_sel_mdl_id,
         glb_fin_mdl, glb_fin_mdl_id,
        file = paste0(glb_out_pfx, "prdnew_dsk.RData"))

sav_fin_mdl <- glb_fin_mdl; sav_sel_mdl <- glb_sel_mdl
#save(sav_fin_mdl, sav_sel_mdl, file=paste0(glb_out_pfx, "sav_mdl.RData"))
# load(file=paste0(glb_out_pfx, "sav_mdl_01.RData"), verbose=TRUE)
# prv_fin_mdl <- sav_fin_mdl; prv_sel_mdl <- sav_sel_mdl
# load(file=paste0(glb_out_pfx, "sav_mdl.RData"), verbose=TRUE)
# cur_fin_mdl <- sav_fin_mdl; cur_sel_mdl <- sav_sel_mdl
# all.equal(cur_fin_mdl, prv_fin_mdl)
# cur_fitobs_df <- cur_fin_mdl$trainingData; prv_fitobs_df <- prv_fin_mdl$trainingData; all.equal(cur_fitobs_df, prv_fitobs_df)
# nrow(cur_fitobs_df); nrow(prv_fitobs_df)
# names(cur_fitobs_df); names(prv_fitobs_df)
# all.equal(cur_fin_mdl$bestTune, prv_fin_mdl$bestTune)

# all.equal(glb_sel_mdl, sav_sel_mdl)
# cur_fitobs_df <- cur_sel_mdl$trainingData; prv_fitobs_df <- prv_sel_mdl$trainingData; all.equal(cur_fitobs_df, prv_fitobs_df)
# head(myget_feats_importance(glb_sel_mdl)); head(myget_feats_importance(sav_sel_mdl))
# head(myget_feats_importance(cur_sel_mdl)); head(myget_feats_importance(prv_sel_mdl))

# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn, 
#     replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
#         "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())

glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)
##                   label step_major step_minor label_minor     bgn     end
## 16     predict.data.new          8          0           0 541.250 561.302
## 17 display.session.info          9          0           0 561.302      NA
##    elapsed
## 16  20.052
## 17      NA

Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.

##                      label step_major step_minor label_minor     bgn
## 5         extract.features          3          0           0  47.050
## 11              fit.models          6          1           1 409.619
## 10              fit.models          6          0           0 342.849
## 7             cluster.data          3          2           2 280.448
## 14       fit.data.training          7          0           0 504.742
## 12              fit.models          6          2           2 477.215
## 16        predict.data.new          8          0           0 541.250
## 1              import.data          1          0           0  23.954
## 9          select.features          5          0           0 331.640
## 15       fit.data.training          7          1           1 533.211
## 13              fit.models          6          3           3 498.303
## 2             inspect.data          2          0           0  40.783
## 8  partition.data.training          4          0           0 330.238
## 3               scrub.data          2          1           1  45.357
## 4           transform.data          2          2           2  46.439
## 6      manage.missing.data          3          1           1 280.282
##        end elapsed duration
## 5  280.281 233.231  233.231
## 11 477.214  67.595   67.595
## 10 409.618  66.769   66.769
## 7  330.237  49.789   49.789
## 14 533.210  28.468   28.468
## 12 498.302  21.087   21.087
## 16 561.302  20.052   20.052
## 1   40.783  16.829   16.829
## 9  342.849  11.209   11.209
## 15 541.249   8.038    8.038
## 13 504.741   6.438    6.438
## 2   45.356   4.574    4.573
## 8  331.639   1.401    1.401
## 3   46.438   1.081    1.081
## 4   47.049   0.610    0.610
## 6  280.448   0.166    0.166
## [1] "Total Elapsed Time: 561.302 secs"

##                                                    label step_major
## 9                              extract.features_bind.DXM          8
## 5                          extract.features_build.corpus          4
## 7                            extract.features_report.DTM          6
## 8                              extract.features_bind.DTM          7
## 3                          extract.features_process.text          3
## 6                           extract.features_extract.DTM          5
## 2                    extract.features_factorize.str.vars          2
## 1                                   extract.features_bgn          1
## 4 extract.features_process.text_reporting_compound_terms          3
##   step_minor label_minor    bgn     end elapsed duration
## 9          0           0 80.382 274.829 194.447  194.447
## 5          0           0 48.741  71.464  22.723   22.723
## 7          0           0 72.831  77.561   4.731    4.730
## 8          0           0 77.562  80.382   2.820    2.820
## 3          0           0 47.145  48.734   1.589    1.589
## 6          0           0 71.465  72.831   1.366    1.366
## 2          0           0 47.074  47.144   0.071    0.070
## 1          0           0 47.057  47.074   0.017    0.017
## 4          1           1 48.735  48.740   0.005    0.005
## [1] "Total Elapsed Time: 274.829 secs"